跳到正文
研踪PaperTrail

YOUR RESEARCH, IN SIGHT

让下一篇重要论文，不再错过。

覆盖计算机全学科，以 AI 为快捷入口。关注方向、作者和关键词，把发现变成持续的研究积累。

部分采集异常
正在读取账号与研究记录…

全站更新概况

+1072新增论文
+845其中 AI 相关
+94已有论文修订
+100新增 AI 导读

arXiv 最近完成扫描：2026-11-09 19:37 · 全站最近入库：2026-11-09 13:22各信源进度 →

统计口径与数据新鲜度

统计 2026-11-08 19:38 — 2026-11-09 19:38（北京时间） · 点击数字查看对应简报；AI 相关包含在新增论文内。

最近完成检查：2026-11-09 19:37最近发现新增版本：2026-11-09 13:22库内论文最新更新时间：2026-11-07 02:59

每分钟刷新已保存的数据。空检查不代表采集异常；新收录可能包含历史论文。AI 导读按新增版本计数。

18,172 已收录论文15,506 AI 相关4,527 有修订112 HF 社区发现

浏览论文库

研究日报 / 周报 →按信源浏览 →

时间范围跟随排序：版本更新、本站收录或首次发表。最新收录可能包含历史论文。部分信源异常，查看状态 →

找到 98 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-09 19:35（北京时间）
新论文 · v1arXiv:2610.34560

CVM-Store: A Secure Storage System for Confidential Virtual Machines

Thore Sommer, Masanori Misono, Dimitrios Stavrakakis, Pramod Bhatotia

原文摘要

Stateful cloud workloads require high-performance persistent storage when running on untrusted cloud infrastructure. Confidential Virtual Machines (CVMs) promise to protect these workloads through hardware-enforced memory encryption and isolation without requiring application changes. However, they protect only data in use; persistent storage lies outside the CVM trust boundary, exposing all stateful data to the cloud provider. Extending the trust boundary to persistent storage presents inherent challenges. High-bandwidth workloads require near-raw NVMe throughput, yet the default kernel CVM I/O stacks are inadequate, and userspace I/O libraries are not designed with CVM use cases in mind. Access patterns and storage semantics vary between workloads, necessitating access-pattern-aware security abstractions rather than uniform block-level encryption. In order to mitigate against rollback, freshness is needed, but CVMs lack hardware primitives for this purpose, requiring alternative solutions. We present CVM-Store, a storage system that co-designs a high-throughput userspace I/O engine with CIF (confidentiality, integrity, freshness) data protection. The key insight is that neither authenticated encryption nor I/O is a bottleneck by itself, but their naive combination is. CVM-Store exploits this by interleaving them, eliminating redundant copies and OS/hypervisor mediation. CIF guarantees are provided as composable primitives that adapt protection to workload access patterns, from which we derive three concrete authenticated abstractions: a KVS, a log, and a filesystem. An asynchronous trusted counter service provides freshness guarantees with epoch-based batching, which enables rollback protection without dedicated hardware support. CVM-Store delivers full CIF guarantees within $\approx$1% (write) and 1-19% (read) of raw NVMe bandwidth for large objects.

cs.CR

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.19705

Trading Agents or Market Crashers? Dissecting Robustness and Security Failures in Academic Financial LLM Trading Schemes

Mengxiao Wang, Nitesh Saxena

原文摘要

Large language model (LLM) agents already trade real capital, and builders copy a few published designs that share models and news feeds, so one shock reaches many agents at once. We present FARSIGHT (Financial Agent Robustness and Security Investigation and Global Holistic Taxonomy), a systematization that rates trading agents on robustness to market stress and on security against source manipulation, agent compromise, and agents acting as attackers, and checks the ratings against released code. Across 15 academic schemes, no paper describes a halt that skips LLM inference or combines source provenance with cross-source corroboration, and the 7 code releases enforce no stop-loss or position limit outside the LLM. Three LLMs sell whole positions on a fake headline and on real hoaxes, including the fake press release behind the 2000 Emulex crash. In a simulated market driven by these decisions, one fake headline crashes the market in all runs with 400 agents, and 400 agents that sell when prices fall nearly double a 2010-sized crash. The pause that U.S. exchanges apply to large stocks starts in no headline run, whereas automatic halts and hard loss limits outside the model remove both effects. Restated as a domain-independent threat model, FARSIGHT maps the same gaps onto agents in healthcare, law, software, and crypto, where compromised agents spread harm as worms and as cascades. We derive 14 open problems and urge deployers to corroborate sources, keep interrupts that bypass the model, and enforce loss limits outside it.

cs.CR · cs.AI · cs.MA

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.00246

Fidelity-Guaranteed Entanglement Routing with Distributed Purification Planning

Anthony Gatti, Anoosha Fayyaz, Prashant Krishnamurthy, Kaushik P. Seshadreesan, Amy Babay

原文摘要

Many quantum-network applications require end-to-end Bell pairs whose fidelity exceeds a request-specific threshold, but existing entanglement routing algorithms either optimize only throughput without regard for fidelity or enforce fidelity guarantees using centralized controllers with global link-state knowledge. We present Q-GUARD, an online entanglement routing algorithm that enforces per-request fidelity thresholds within a distributed protocol model in which nodes exchange link-state information only with their $k$-hop neighbors. After link outcomes are realized in each slot, Q-GUARD builds per-link purification cost tables from realized Bell pairs, allocates per-hop fidelity targets using a Werner-state equal-split rule, and selects between candidate path segments using a segment-local expected-goodput (EXG) metric that jointly accounts for swap success, purification overhead, and resource availability. We also introduce Q-GUARD-WS, an extension that exploits per-link hardware quality estimates to allocate purification effort non-uniformly across hops. On synthetic 100-node topologies with heterogeneous link fidelity and stochastic BBPSSW purification, Q-GUARD raises the qualified success rate from under 20\% to over 85\% on 4-hop paths and nearly doubles the qualified service radius in Euclidean distance relative to throughput-only and naive-purification baselines, while Q-GUARD-WS provides additional throughput gains under high hardware heterogeneity.

cs.NI · quant-ph

查看论文
arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1112ACL Anthology · ACL 2026

Truth or Sophistry? LoFa: A Benchmark for LLM Robustness Against Logical Fallacies

Xudong Shen, li Yuan, Ye Chen, Xin Wu South China University of Technology, Yi Cai South China University of Technology, Zhiyong Wu Tsinghua University

原文摘要

While Large Language Models (LLMs) exhibit strong semantic capabilities, their resilience to manipulative linguistic patterns such as logical fallacies remains an underexplored area. Prior work has focused on the ability of LLMs to identify or classify fallacies, but their robustness against these fallacies in persuasive contexts remains largely unexplored.To address this gap, we introduce LoFa (Logical Fallacy), a comprehensive benchmark to evaluate LLM robustness against fallacies. We first construct the LoFa dataset via a multi-agent pipeline, pairing factual questions with fallacious arguments. Then, we develop a multi-round debate framework to assess model resilience under sustained attacks.Furthermore, to disentangle robustness from a model’s inherent knowledge limitations, we propose a new metric, LFR@k (Logical Fallacy Resistance), to quantify performance. Our experiments reveal that different LLMs exhibit varied robustness to distinct types of fallacies, highlighting unique vulnerability profiles across models.

cs.CL · cs.MA

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1311ACL Anthology · ACL 2026

KoCo-Bench: Can Large Language Models Leverage Domain Knowledge in Software Development?

Xue Jiang, Ge Li Peking University, Jiaru Qian, Xianjie Shi, Chenjie Li Peking University, Hao Zhu, Ziyu Wang Peking University, Jielun Zhang Peking University 等 14 位作者

原文摘要

Large language models (LLMs) excel at general programming but struggle with domain-specific software development. This gap motivates research into domain specialization methods that enable LLMs to learn and utilize domain knowledge and data. However, existing domain-specific code benchmarks focus on assessing what knowledge LLMs possess rather than how they acquire and apply new knowledge, lacking explicit knowledge corpora for developing domain specialization methods. To this end, we present KOCO-bench, a novel benchmark designed for evaluating domain specialization methods in real-world software development. KOCO-bench contains 6 emerging domains with 11 software frameworks and 25 projects, featuring curated knowledge corpora alongside multi-granularity evaluation tasks including domain code generation (from function-level to project-level with rigorous test suites) and domain knowledge understanding (via multiple-choice Q A). Unlike previous benchmarks that only provide test sets for direct evaluation, KOCO-bench requires acquiring and applying diverse domain knowledge (APIs, rules, constraints, etc.) from the corpora to solve evaluation tasks. Our evaluations reveal that KOCO-bench poses significant challenges to state-of-the-art LLMs. Even with domain specialization methods (e.g., SFT, RAG, kNN-LM) applied, improvements remain marginal. Best-performing coding agent, Claude Code, achieves only 34.2%, highlighting the urgent need for more effective domain specialization methods. We release KOCO-bench, evaluation code, and baselines to advance further research at https://github.com/jiangxxxue/KOCO-bench.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1451ACL Anthology · ACL 2026

Disentangling Reasoning Logic to Resolve Explicit Knowledge Conflicts

Xianda Zheng University of Auckland, Zijian Huang University of Auckland, Meng-Fen Chiang, Jiamou Liu The University of Auckland, Yuan Fang Singapore Management University, Michael J. Witbrock University of Auckland, Kaiqi Zhao Harbin Institute of Technology, Shenzhen

原文摘要

Explicit knowledge conflicts, where retrieved contexts contain contradictory information, have become increasingly prevalent as Large Language Models (LLMs) integrate diverse data sources. The core challenge lies in the complexity of entangled narratives and the heterogeneity of conflict cases, which impose excessive demands on the reasoning capabilities of standard models. To address this, we propose Knowledge Conflict Reasoning (KCR), a framework that adjudicates conflicts by structuring the underlying logic. KCR first disentangles conflicting contexts into distinct sets of reasoning traces, utilizing both textual and graph-based representations, to simplify comprehension. It then employs a Reinforcement Learning with Verifiable Rewards (RLVR) paradigm, guiding the model to internalize a reasoning process that maximizes logical consistency while actively suppressing spurious reasoning paths derived from contradictory contexts. Extensive experiments demonstrate that KCR yields substantial improvements: a KCR-enhanced 7B model surpasses the performance of baselines equipped with top-tier closed-source models such as GPT-4o and GPT-5.1.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1461ACL Anthology · ACL 2026

QA-MoE: Towards a Continuous Reliability Spectrum with Quality-Aware Mixture of Experts for Robust Multimodal Sentiment Analysis

Yitong Zhu, Yuxuan Jiang, Guanxuan Jiang Hong Kong University of Science and Technology (Guangzhou), Bojing Hou, Peng Yuan Zhou, Ge Lin, Yuyang Wang

原文摘要

Multimodal Sentiment Analysis (MSA) aims to infer human sentiment from textual, acoustic, and visual signals. In real-world scenarios, however, multimodal inputs are often compromised by dynamic noise or modality missingness. Existing methods typically treat these imperfections as discrete cases or assume fixed corruption ratios, which limits their adaptability to continuously varying reliability conditions. To address this, we first introduce a Continuous Reliability Spectrum to unify missingness and quality degradation into a single framework. Building on this, we propose QA-MoE, a Quality-Aware Mixture-of-Experts framework that quantifies modality reliability via self-supervised aleatoric uncertainty. This mechanism explicitly guides expert routing, enabling the model to suppress error propagation from unreliable signals while preserving task-relevant information. Extensive experiments indicate that QA-MoE achieves competitive or state-of-the-art performance across diverse degradation scenarios and exhibits a promising One-Checkpoint-for-All property in practice.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.164ACL Anthology · ACL 2026

CODERL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment

Xue Jiang, Yihong Dong, Mengyang Liu Independent Researcher, Deng Hongyi, Tian Wang, Yongding Tao Peking University, Zhi Jin Peking University, Wenpin Jiao Peking University 等 9 位作者

原文摘要

While Large Language Models (LLMs) excel at code generation by learning from vast code corpora, a fundamental semantic gap remains between their training on textual patterns and the goal of functional correctness, which is governed by formal execution semantics. Reinforcement Learning with Verifiable Rewards (RLVR) approaches attempt to bridge this gap using outcome rewards from executing test cases. However, solely relying on binary pass/fail signals is inefficient for establishing a well-aligned connection between the textual representation of code and its execution semantics, especially for subtle logical errors within the code. In this paper, we propose CODERL+, a novel approach that integrates execution semantics alignment into the RLVR training pipeline for code generation. CODERL+ enables the model to infer variable-level execution trajectory, providing a direct learning signal of execution semantics. CODERL+ can construct execution semantics alignment directly using existing on-policy rollouts and integrates seamlessly with various RL algorithms. Extensive experiments demonstrate that CODERL+ outperforms post-training baselines (including RLVR and Distillation), achieving a 4.6% average relative improvement in pass@1. CODERL+ generalizes effectively to other coding tasks, yielding 15.5% and 4.4% higher accuracy on code-reasoning and test-output-generation benchmarks, respectively. CODERL+ shows strong applicability across diverse RL algorithms and LLMs. Furthermore, probe analyses provide compelling evidence that CODERL+ strengthens the alignment between code’s textual representations and its underlying execution semantics.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.165ACL Anthology · ACL 2026

Saber: Efficient Sampling with Adaptive Acceleration and Backtracking Enhanced Remasking for Diffusion Language Model in Code Generation

Yihong Dong, Zhaoyu Ma, Xue Jiang, Zhiyuan Fan Northwest Polytechnical University Xi'an, Jiaru Qian, Yongmin Li Peking University, Jianha Xiao, Zhi Jin Peking University 等 9 位作者

原文摘要

Diffusion language models (DLMs) are emerging as a compelling alternative to the dominant autoregressive paradigm, offering inherent advantages in parallel generation and bidirectional context modeling. However, for the tasks with strict structural constraints such as code generation, DLMs face a critical trade-off between inference speed and output quality, where accelerating generation by reducing sampling steps often leads to catastrophic performance collapse.We find that the fundamental reasons are: 1) the generation difficulty is uneven in the structured sequence decoding steps, making DLM’s static acceleration strategy suboptimal; 2) the context of tokens generated by DLM evolves continuously, causing early high-confidence predictions to turn into irreversible errors.In this paper, we introduce efficient Sampling with Adaptive acceleration and Backtracking Enhanced Remasking (i.e., Saber), a novel training-free sampling algorithm for DLMs that the first to improve both inference speed and output quality in code generation. Saber dynamically adjusts the number of tokens unmasked per step based on the model’s evolving confidence, and utilizes a backtracking mechanism to revert tokens whose confidence drops as new context emerges, with its effectiveness supported by theoretical analysis.Extensive experiments on multiple mainstream code generation benchmarks show that Saber boosts Pass@1 accuracy by an average of 1.9% over mainstream DLM sampling methods, while achieving an average 251.4% inference speedup. By leveraging the inherent advantages of DLMs, our work significantly narrows the performance gap with autoregressive models in code generation.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1994ACL Anthology · ACL 2026

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization

Yihong Dong, Xue Jiang, Yongding Tao Peking University, Huanyu Liu Peking University, Kechi Zhang Peking University, Lili Mou University of Alberta, Rongyu Cao, Yingwei MA Alibaba Group 等 14 位作者

原文摘要

Reinforcement Learning with Verifiable Reward (RLVR) has significantly advanced the complex reasoning abilities of Large Language Models (LLMs). However, it struggles to break through the inherent capability boundaries of the base LLM, due to its essentially on-policy strategy coupled with LLM’s immense action space and sparse reward. Critically, RLVR can lead to the capability boundary collapse, narrowing the LLM’s problem-solving scope. To address this problem, we propose R-PLUS, a novel hybrid-policy optimization approach for LLMs that synergizes internal exploitation with external data to achieve stronger reasoning capabilities and surpass the boundaries of base models. R-PLUS integrates two core components, i.e., Multiple Importance Sampling to address distributional mismatch from external data, and Exploration-Based Advantage Function to guide the model towards high-value, unexplored reasoning paths. We provide both theoretical analysis and extensive experiments to demonstrate the superiority and generalizability of our approach. Compared with existing RLVR methods, R-PLUS achieves 1) state-of-the-art performance on six math reasoning benchmarks; 2) superior performance on six out-of-distribution reasoning tasks; 3) consistent and significant gains across diverse model families, with average relative improvements up to 69.2%. Moreover, the analysis of Pass@k curves indicates that R-PLUS effectively resolves the capability boundary collapse problem.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.333ACL Anthology · ACL 2026

ET-Agent: Incentivizing Effective Tool-Integrated Reasoning Agent via Behavior Calibration

Yifei Chen, Guanting Dong, Zhicheng Dou Renmin University of China

原文摘要

Large Language Models (LLMs) can extend their parameter knowledge limits by adopting the Tool-Integrated Reasoning (TIR) paradigm. However, existing LLM-based agent training framework often focuses on answers’ accuracy, overlooking specific alignment for behavior patterns. Consequently, agent often exhibits ineffective actions during TIR tasks, such as redundant and insufficient tool calls. How to calibrate erroneous behavioral patterns when executing TIR tasks, thereby exploring effective trajectories, remains an open-ended problem. In this paper, we propose ET-Agent, a training framework for calibrating agent’s tool-use behavior through two synergistic perspectives: Self-evolving Data Flywheel and Behavior Calibration Training. Specifically, we introduce a self-evolutionary data flywheel to generate enhanced data, used to fine-tune LLM to improve its exploration ability. Based on this, we implement an two-phases behavior-calibration training framework. It is designed to progressively calibrate erroneous behavioral patterns to optimal behaviors. Further in-depth experiments confirm the superiority of ET-Agent across multiple dimensions, including correctness, efficiency, reasoning conciseness, and tool execution accuracy. Our ET-Agent framework provides practical insights for research in the TIR field. Codes can be found in https://github.com/RUC-NLPIR/ET-Agent

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.953ACL Anthology · ACL 2026

VideoCuRL: Video Curriculum Reinforcement Learning with Orthogonal Difficulty Decomposition

Hongbo Jin, Kuanwei Lin, Wenhao Zhang, Yichen Jin Peking University, Ge Li Peking University

原文摘要

Reinforcement Learning (RL) is crucial for empowering Video-LLMs with complex spatiotemporal reasoning. However, current RL paradigms predominantly rely on random data shuffling or naive curriculum strategies based on scalar difficulty metrics. We argue that scalar metrics fail to disentangle two orthogonal challenges in video understanding: Visual-Temporal Perception Load and Cognitive Reasoning Depth. To address this, we propose VideoCuRL, a novel framework that decomposes difficulty into these two axes. We employ efficient, training-free proxies—optical flow/keyframe entropy for visual complexity and Calibrated Surprisal for cognitive complexity—to map data onto a 2D curriculum grid. A competence-aware Diagonal Wavefront strategy then schedules training from base alignment to complex reasoning. Furthermore, we introduce Dynamic Sparse KL and Structured Revisiting to stabilize training against reward collapse and catastrophic forgetting. Extensive experiments show that VideoCuRL surpasses strong RL baselines on reasoning (+2.5% on VSI-Bench) and perception (+2.9% on VideoMME) tasks. Notably, VideoCuRL eliminates the prohibitive inference overhead of generation-based curricula, offering a scalable solution for robust video post-training.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1CVF · CVPR 2026:cvf:046d89883ce07b9d4d6e41503ed61441CVF · CVPR 2026

TransPrune: Token Transition Pruning for Efficient Large Vision-Language Model

Li, Ao, Duan, Yuxiang, Zhang, Jinghui, Ma, Congbo, Xie, Yutong, Carneiro, Gustavo, Yaqub, Mohammad, Wang, Hu

原文摘要

Large Vision-Language Models (LVLMs) have advanced multimodal learning but face high computational cost issues due to the input of large number of visual tokens, motivating token pruning to improve inference efficiency.The key challenge lies in identifying which tokens are truly important.Most existing approaches rely on attention- or similarity-based criteria to estimate token importance.However, they inherently suffer from certain limitations, such as being task-agnostic and exhibiting positional bias.In this work, we explore a new perspective on token importance assignment based on token transitions in LVLMs, where token transitions are defined as the changes in token representations occurring as they propagate through the model's modules.We observe that the transition of token representations provides a meaningful signal of semantic information.Based on this insight, we propose TransPrune, a training-free and efficient token pruning method.Specifically, TransPrune progressively prunes tokens by assessing their importance through a combination of Token Transition Variation (TTV), which measures changes in both the magnitude and direction of token representations; as well as Instruction-Guided Attention (IGA), which measures how strongly the instruction attends to visual tokens via attention.Extensive experiments on various LVLM architectures, such as LLaVA-v1.5, LLaVA-Next and Qwen2.5-VL, demonstrate that TransPrune maintains comparable multimodal performance while reducing inference TFLOPs by more than half. The code is available at https://github.com/liaolea/TransPrune.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:11291b17763c3b6f07aa59d9eb445c33CVF · CVPR 2026

Ego: Embedding-Guided Personalization of Vision-Language Models

Seifi, Soroush, Gardier, Simon, Dorovatas, Vaggelis, Reino, Daniel Olmeda, Aljundi, Rahaf

原文摘要

AI assistants that support humans in daily life are becoming increasingly feasible, driven by the rapid advancements in multimodal language models. A key challenge lies in overcoming the generic nature of these models to deliver personalized experiences. Existing approaches to personalizing large vision language models often rely on additional training stages, which limit generality and scalability, or on engineered pipelines with external pre-trained modules, which hinder deployment efficiency. In this work, we propose an efficient personalization method that leverages the model's inherent ability to capture personalized concepts. Specifically, we extract visual tokens that predominantly represent the target concept by utilizing the model's internal attention mechanisms. These tokens serve as a memory of that specific concept, enabling the model to recall and describe it when it appears in test images. We conduct a comprehensive and unified evaluation of our approach and SOTA methods across various personalization settings including single-concept, multi-concept, and video personalization, demonstrating strong performance gains with minimal personalization overhead.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:1542773526087bef5b6ba320ff014f4dCVF · CVPR 2026

Adaptive 3D Perception for Small Aerial Targets Under Sparse Sampling via Reinforcement Learning

Yuan, Shenghai, Yihan, Wei, Yee, Jason, Qiao, Zhuoran, lou, boyang, Hu, Enwen

原文摘要

Detecting small aerial targets (SATs) in long-range LiDAR is challenging because motion causes extreme variations in point density, breaking fixed-voxel and static-threshold assumptions in standard 3D detection and tracking. To address the challenges, we introduce A3PRL, an RL-driven adaptive perception framework that closes the loop between LiDAR sensing and tracking. A3PRL uses a sparsity-aware proposal stage with Temporal Dispersion Signatures and velocity-change cues, and a lightweight 5D policy that jointly adjusts voxel resolution, detection sensitivity, and association gating from label-free statistics of sparsity, foreground acceptance, and track stability. The policy is trained with privileged ground-truth trajectories to optimize a reward balancing geometric accuracy, temporal stability, and regularized acceptance, but runs fully label-free at test time. On the public MMAUD benchmark, training on V1 and evaluating on unseen V2/V3, A3PRL reduces 3D localization error by about 19% over its non-RL counterpart and consistently outperforms LiDAR-only and multimodal baselines under both day and night conditions. The same policy is able to transfer to other SAT datasets with heterogeneous scan patterns, maintaining accurate and stable trajectories.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:18b9d43777264b769e905fd284bb3da0CVF · CVPR 2026

KAMP: Knowledge-Anchored Multimodal Pretraining Framework for Medical Image Representation

Huang, Feiyu, Li, Jia, Chen, Zhao, Wu, Yang, Cao, Caleb Chen, Chen, Lei

原文摘要

Cross-modal biomedical signals such as pathology and genomics can provide richer and more robust semantic guidance for medical image representation learning. However, the availability of such guidance remains limited, as privacy constraints and acquisition costs severely restrict access to medical images paired with other biomedical data. A further challenge lies in modality discrepancy, which introduces intra-modal statistical bias and cross-modal noise, thereby degrading the quality of medical image representations. To address these challenges, we propose KAMP, a large language model (LLM)-driven multimodal pretraining framework for medical image representation learning. KAMP leverages textual priors as the semantic anchor to enhance medical image representations and align them with multimodal biomedical representations, enabling the learning of rich and robust features even when paired data are scarce. KAMP operates in three stages. First, the LLM generates personalized diagnostic knowledge from patient clinical text and imaging metadata. This knowledge is injected as a prior to enrich medical image representations and serves as a semantic anchor to reduce the representation gap between medical images and other biomedical modalities. Second, the LLM is optimized using Group Relative Policy Optimization (GRPO), with the cross-modal aligner pretrained in the first stage serving as the reward model. Third, the refined knowledge is used to retrain the cross-modal aligner, yielding more robust medical image representations while mitigating bias and noise introduced by other modalities. Comprehensive evaluations on brain, bladder, and liver cancer datasets demonstrate that KAMP outperforms existing methods in most downstream few-shot classification tasks.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:24e7fb10f4a0763c6de38ad2cfa1d321CVF · CVPR 2026

FlexAvatar: Learning Complete 3D Head Avatars with Partial Supervision

Kirschstein, Tobias, Giebenhain, Simon, Nießner, Matthias

原文摘要

We introduce FlexAvatar, a method for creating high-quality and complete 3D head avatars from a single image. A core challenge lies in the limited availability of multi-view data and the tendency of monocular training to yield incomplete 3D head reconstructions. We identify the root cause of this issue as the entanglement between driving signal and target viewpoint when learning from monocular videos. To address this, we propose a transformer-based 3D portrait animation model with learnable data source tokens, so-called bias sinks, which enables unified training across monocular and multi-view datasets. This design leverages the strengths of both data sources during inference: strong generalization from monocular data and full 3D completeness from multi-view supervision. Furthermore, our training procedure yields a smooth latent avatar space that facilitates identity interpolation and flexible fitting to an arbitrary number of input observations. In extensive evaluations on single-view, few-shot, and monocular avatar creation tasks, we verify the efficacy of FlexAvatar. Many existing methods struggle with view extrapolation while FlexAvatar generates complete 3D head avatars with realistic facial animations.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:2510b0337465b45501546283a7c563e0CVF · CVPR 2026

Hierarchical Enhancement of Semantic Priors for Disentangled Text-Driven Motion Generation

Lv, Wenhan, Wang, Shaopan, Wu, Xiangyu, Hang, Tianchu, Jian, Zhongquan, Wu, Qingqiang

原文摘要

Text-to-motion generation aims to synthesize realistic and semantically aligned 3D human motions from natural language descriptions. Existing diffusion-based methods often rely on isotropic latent priors and shallow cross-modal supervision, which lead to semantic entanglement, limited controllability, and poor interpretability. We propose HESP, a unified diffusion framework that hierarchically enhances semantic priors for disentangled text-driven motion generation. At its core, HESP introduces an Adaptive Gaussian Variational Autoencoder (AG-VAE) that structures the latent motion manifold into multiple semantically coherent submanifolds, enabling interpretable and controllable motion representations. To further bridge linguistic and kinematic semantics, we design a Dynamic Cross-Modal Memory (DCMM) module for adaptive semantic fusion and a Hierarchical Cross-Modal Attention (HCA) mechanism to capture multi-level text-motion correspondences. Extensive experiments on HumanML3D and KIT-ML demonstrate that HESP consistently outperforms state-of-the-art baselines such as SALAD, MoMask, and MDM, achieving improvement while maintaining higher diversity and physical plausibility. Moreover, the structured latent space of HESP provides interpretable clusters that reveal clear semantic boundaries among different motion categories.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:25e6ed51c790cdba924a708617a74daaCVF · CVPR 2026

High-Fidelity Mobile Avatars with Pruned Local Blendshapes

Zhan, Youyi, Wang, He, Shao, Tianjia, Zhou, Kun

原文摘要

We propose a method to reconstruct high-fidelity human avatars from multi-view video that can run on mobile devices. Many works can model high-quality Gaussian-based full-body avatars from multi-view video. However, these methods require heavy computation to obtain pose-dependent appearance, making deployment on mobile devices very difficult. Recent methods distill from pretrained models and model pose-dependent nonlinear Gaussian attributes by linearly combining global pose features with blendshapes. Although they can run on mobile devices, they suffer some loss of detail. We observe that nearby Gaussians are often highly correlated within a local region of the body, and can be linearly modeled with less error. Therefore, we use local linear blendshapes in small body parts to capture global nonlinear changes of Gaussian attributes. To further reduce computation and model size, we propose to remove blendshapes for Gaussians whose attributes change little, yielding a minimal blendshape representation. Our method is an end-to-end training method without a pretrained model. To make it run on multiple devices, we implement our method using WebGPU. Experiments show that our method can render high-quality human avatars with better details, and can reach 120 FPS at 2K resolution on mobile devices.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:33600b68750d093177692c45f1d22b83CVF · CVPR 2026

MimiCAT: Mimic with Correspondence-Aware Cascade-Transformer for Category-Free 3D Pose Transfer

Chai, Zenghao, Tang, Chen, Wong, Yongkang, Yang, Xulei, Kankanhalli, Mohan

原文摘要

3D pose transfer aims to transfer the pose-style of a source mesh to a target character while preserving both the target's geometry and the source's pose characteristic. Existing methods are largely restricted to characters with similar structures and fail to generalize to category-free settings (e.g., transferring a humanoid's pose to a quadruped). The key challenge lies in the structural and transformation diversity inherent in distinct character types, which often leads to mismatched regions and poor transfer quality. To address these issues, we first construct a million-scale pose dataset across hundreds of distinct characters. We further propose MimiCAT, a cascade-transformer model designed for category-free 3D pose transfer. Instead of relying on strict one-to-one correspondence mappings, MimiCAT leverages semantic keypoint labels to learn a novel soft correspondence that enables flexible many-to-many matching across characters. The pose transfer is then formulated as a conditional generation process, in which the source transformations are first projected onto the target through soft correspondence matching and subsequently refined using shape-conditioned representations. Extensive qualitative and quantitative experiments demonstrate that MimiCAT generalizes plausible poses across diverse character morphologies, surpassing prior approaches restricted to narrow-category transfer (e.g., humanoid-to-humanoid).

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:46c898c875c19ea98c0ad7b2af990c56CVF · CVPR 2026

Self-guided Semantic Inspection for Zero-Shot Composed Image Retrieval

Zhang, Jingjing, Zhang, Lei, Fu, Zheren, Hu, Bo, Mao, Zhendong

原文摘要

Zero-Shot Composed Image Retrieval (ZS-CIR) aims to retrieve target images using a composed query of a reference image and a textual modification, without relying on triplet-based supervision. As the two inputs describe related but semantically unaligned information, the key challenge lies in interpreting their cross-modal discrepancy to infer the user's intended semantic modification. Existing ZS-CIR methods mainly adopt a consistency-driven paradigm, training on semantically aligned image-text pairs with alignment or reconstruction objectives. This paradigm enforces cross-modal agreement but overlooks the semantic discrepancies between modalities that naturally arise during inference. To address this issue, we propose DiffComp (Differentiate-then-Compose), a difference-driven self-supervised framework that actively induces and exploits cross-modal discrepancies during training. It stimulates the model to perceive and reconcile semantic differences across visual and textual modalities, thereby improving consistency between training and inference. The framework consists of three components: Contextual Semantic Super-patches that provide localized and coherent visual representations for downstream perception and composition; Phrase-guided Masking that selectively removes text-aligned visual cues to induce controlled cross-modal discrepancies; and Difference-aware Composition that adaptively integrates visual and textual features according to their degree of semantic difference. Extensive experiments on four ZS-CIR benchmarks show that DiffComp achieves state-of-the-art performance and strong generalization. Our code will be released at https://github.com/Orange1999/DiffComp.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:678018cfeeb57b11a90feaf8885c8afcCVF · CVPR 2026

Fine-Grained Multi Image Object Hallucination Benchmark

Min, Joonki, Kim, Chaeyun, Choi, Hyungwook, Kim, Yejin, Kim, Kihyun, Jo, Yohan, Lee, Joonseok

原文摘要

Multimodal Large Language Models (MLLMs) are increasingly deployed in multi-image scenarios requiring complex reasoning across visual contexts. However, current MLLMs remain fundamentally limited by object hallucination--generating plausible yet factually inconsistent descriptions about objects. Existing benchmarks, designed primarily for single-image settings or providing only high-level multi-image assessments, cannot systematically diagnose how visual complexity and reasoning demands trigger hallucination. To address this gap, we introduce MIOH, a fine-grained multi-image object hallucination benchmark that systematically evaluates object hallucination across four foundational tasks (existence, counting, attribute, position) through three multi-image reasoning patterns (comprehensive, comparative, selective) under three controlled adversarial pressures (visual context scale, perceptual difficulty, contextual bias). Through evaluation of 29 models, we reveal that even state-of-the-art systems like GPT-5 and Gemini-2.5-Pro exhibit distinct failure patterns across different reasoning patterns and tasks. Our evaluation reveals that hallucination stems not merely from perceptual failures but from integration-stage limitations when maintaining object representations across multiple images. MIOH provides a controlled framework for analyzing multi-image object hallucination and serves as a critical evaluation tool for developing more reliable multimodal AI systems.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:8dc707007d5877169df413d9ddef7d59CVF · CVPR 2026

TopoMesh: High-Fidelity Mesh Autoencoding via Topological Unification

Luo, Guan, Li, Xiu, Chen, Rui, Yi, Xuanyu, Lin, Jing, Chen, Chia Hao, Liu, Jiahang, Zhang, Song-Hai 等 9 位作者

原文摘要

The dominant paradigm for high-fidelity 3D generation relies on a VAE-Diffusion pipeline, where the VAE's reconstruction capability sets a firm upper bound on generation quality. A fundamental challenge limiting existing VAEs is the representation mismatch between ground-truth meshes and network predictions: GT meshes have arbitrary, variable topology, while VAEs typically predict fixed-structure implicit fields (e.g., SDF on regular grids). This inherent misalignment prevents establishing explicit mesh-level correspondences, forcing prior work to rely on indirect supervision signals such as SDF or rendering losses. Consequently, fine geometric details, particularly sharp features, are poorly preserved during reconstruction. To address this, we introduce TopoMesh, a sparse voxel-based VAE that unifies both GT and predicted meshes under a shared Dual Marching Cubes (DMC) topological framework. Specifically, we convert arbitrary input meshes into DMC-compliant representations via a remeshing algorithm that preserves sharp edges using an Linfinity distance metric. Our decoder outputs meshes in the same DMC format, ensuring that both predicted and target meshes share identical topological structures. This establishes explicit correspondences at the vertex and face level, allowing us to derive explicit mesh-level supervision signals for topology, vertex positions, and face orientations with clear gradients. Our sparse VAE architecture employs this unified framework and is trained with Teacher Forcing and progressive resolution training for stable and efficient convergence. Extensive experiments demonstrate that TopoMesh significantly outperforms existing VAEs in reconstruction fidelity, achieving superior preservation of sharp features and geometric details, as shown in Fig. 1.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:a86439954c9fae1202024b7e25e208e7CVF · CVPR 2026

Beyond Explicit Language: Plug-and-Play Visual-to-Linguistic Modeling Toward General Object Tracking

Lan, Kaiyang, Cui, Ying, Jing, Chenchen, Zheng, Jianwei, Guo, Dongyan

原文摘要

Natural language provides valuable auxiliary information for enhancing visual object tracking. While existing vision-language tracking methods explicitly leverage linguistic descriptions to aid tracking, they suffer from two critical limitations: the inability to dynamically adapt descriptions to the moving target and changing context; and the strong dependency on language input may causes failure when text is unavailable. To address the issues, we design a simple yet effective plug-and-play module that leverages linguistic assistance implicitly, without requiring explicit language input. The proposed textual inversion module converts visual features from template and search regions into text tokens in the CLIP text embedding space. It effectively inverts visual representations into linguistic forms, integrating contextual information from the both template and search region. The linguistic cues are then injected into the visual feature space via a multi-layer semantic injection mechanism. The design enhances the completeness of cross-modal feature representations and the accuracy of inter-modal semantic alignment, thus enabling dynamically updated linguistic information guidance for general object tracking. Extensive experiments demonstrate the effectiveness of our proposed method. We integrate the proposed module into several advanced trackers and evaluate on both visual and vision-language tracking datasets, including MCITrack, DUTrack, and SeqTrack. By training only the newly introduced module and the corresponding decoder, the proposed approach achieves significant performance gains with minimal computational overhead. Code will be made publicly available.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:af3988412428fe5e983d51601ac5aa16CVF · CVPR 2026

Focal-General Diffusion Model with Semantic Consistent Guidance for Sign Language Production

Yu, Yiheng, Liu, Sheng, Feng, Yuan, Jin, Zhelun, Jiang, Yining, Xu, Min

原文摘要

Sign Language Production (SLP) aims to translate spoken language into sign sequences, where the main challenge lies in generating coherent and natural poses from discrete glosses (G2P). Existing G2P methods typically treat each pose as an indivisible unit, limiting their ability to capture fine-grained joint-level dependencies and thus degrading pose quality. To address this, we propose the Focal-General Diffusion Model (FGDM), characterized by a pioneering two-stage denoising framework that harmonizes local joint-level dependencies and global coherence. Specifically, in the Focal stage, a novel Adaptive Sign GCN (ASGCN) adaptively models each pose based on contextual correlations, skeletal topology, and semantic conditions, ensuring precise generation of local details. In the General stage, a Transformer-based module refines the entire pose sequence to enhance global coherence and naturalness. Moreover, we introduce a Semantic Consistent Guidance (SCG) mechanism that seamlessly integrates semantic supervision into diffusion training, enforcing tighter alignment between generated pose sequences and their intended gloss semantics. Extensive experiments on PHOENIX14T and USTC-CSL demonstrate that FGDM achieves SOTA performance. Our project page is available at https://yuyiheng-eu.github.io/fgdm/.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:be7576d09384a19b964f052627822d5aCVF · CVPR 2026

Compositional Transformation Reasoning for Composed Video Retrieval

Huang, Sihong, Wu, Jiaxin, Jiang, Dongmei, Cai, Yi, Wang, Yaowei, Wei, Xiaoyong

原文摘要

Composed Video Retrieval aims to retrieve a target video given a reference video and a textual modification describing the desired change. The core challenge lies in modeling compositional multimodal transformations, i.e., how entities, actions, and scenes evolve across video and language modalities in response to fine-grained textual edits. Existing methods address this issue by training on large-scale video-text-video triplets or by generating dense textual descriptions to capture subtle visual differences. However, these supervised approaches often rely on noisy web-scale data and dataset-specific correspondences, leading to overfitting and limited generalization in diverse or fine-grained scenarios, while also failing to effectively model compositional and temporal transformations. We propose Multi-objective Reasoning (MoRe), a zero-shot framework based on MLLMs for multi-objective candidate selection and fine-grained transformation reasoning. Our method decomposes the compositional transformation into three complementary reasoning dimensions, i.e., entity, action, and scene, and performs pairwise candidate reasoning to explicitly capture semantic evolution over time. Furthermore, we introduce a recall-oriented multi-objective candidate selection module that identifies high-quality retrieval targets by jointly balancing visual, textual, and multimodal similarities before transformation reasoning. Experiments on EgoCVR and WebVid-CoVR demonstrate the effectiveness of our method over state-of-the-art approaches under the zero-shot setting, with R@1 improvements of +5.8 and +10.8, respectively.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:c6bd237f15814480e40853d72fccc293CVF · CVPR 2026

How to Take a Memorable Picture? Empowering Users with Actionable Feedback

Laiti, Francesco, Talon, Davide, Staiano, Jacopo, Ricci, Elisa

原文摘要

Image memorability, i.e., how likely an image is to be remembered, has traditionally been studied in computer vision either as a passive prediction task, with models regressing a scalar score, or with generative methods altering the visual input to boost the image likelihood of being remembered. Yet, none of these paradigms supports users at capture time, when the crucial question is how to improve a photo memorability. We introduce the task of **Mem**orability **Feed**back (**MemFeed**), where an automated model should provide actionable, human-interpretable guidance to users with the goal to enhance an image future recall. We also present **MemCoach**, the first approach designed to provide concrete suggestions in natural language for memorability improvement (e.g., "emphasize facial expression," "bring the subject forward"). Our method, based on Multimodal Large Language Models (MLLMs), is training-free and employs a teacher-student steering strategy, aligning the model internal activations toward more memorable patterns learned from a teacher model progressing along least-to-most memorable samples. To enable systematic evaluation on this novel task, we further introduce **MemBench**, a new benchmark featuring sequence-aligned photoshoots with annotated memorability scores. Our experiments, considering multiple MLLMs, demonstrate the effectiveness of MemCoach, showing consistently improved performance over several zero-shot models. The results indicate that memorability can not only be predicted but also taught and instructed, shifting the focus from mere prediction to actionable feedback for human creators. Dataset and code will be publicly released upon publication.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:d6710d7b7499ab4a194e53d7b3d7b75aCVF · CVPR 2026

EXOTIC: External Vision-driven Incomplete Multi-view Classification

Xu, Shilin, Peng, Dezhong, Ren, Zhenwen, Sun, Yuan

原文摘要

Due to sensor failures and occlusions during data acquisition, multi-view data often suffer from partial missing samples, thereby producing incomplete multi-view data. Recently, Incomplete Multi-View Classification (IMVC) has become one of the research hot topics, where numerous IMVC methods have been proposed. Although these methods have achieved promising performance by exploiting internal semantic information from partially observed data, they primarily rely on limited internal supervision for view completion. Clearly, this largely constrains their performance ceiling. To overcome this limitation, we propose an EXternal visiOn-driven incomplete mulTi-vIew Classification (EXOTIC) paradigm that incorporates external vision knowledge as semantic guidance, thereby assisting in imputing incomplete views. To the best of our knowledge, it is the first work that leverages external vision knowledge as supervision signals, thereby guiding missing-view completion. Specifically, we first introduce an external vision knowledge library based on a pre-trained vision-language model. Then, we design a Knowledge Filtering module to adaptively select task-relevant knowledge. Afterwards, we present a Knowledge Purification module to align external knowledge with internal representations. Finally, we propose External Completion that leverages the refined knowledge to impute missing views, thereby enhancing the classification decision ability. Extensive experiments on multiple incomplete multi-view datasets demonstrate that the proposed EXOTIC consistently outperforms existing methods, especially under high missing rates.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:dda52b9acb5b95c11ee00d522885e78aCVF · CVPR 2026

Global Underwater Geolocation from Time-Lapse Polarization Imagery

Aghajanzadeh, Sara, Bai, Xiaoyang, Zhu, Zhongmin, Forsyth, David, Gruev, Viktor

原文摘要

It is extremely hard for an underwater agent to know where it is. Satellite signals disappear within centimeters of the surface; acoustic baselines require heavy infrastructure to instrument small regions. The polarization of the sky, visible underwater, reveals the elevation of the sun. The pattern of elevation over the day reveals location to an agent with a clock. However, recovering elevation from polarization images is very difficult. State-of-the-art (SOTA) geolocalization methods can localize well for locations where they have seen data, but accuracy collapses when the data comes from a new location. Our physics-guided synthesis pipeline expands a huge library of polarization images from a small set of sites to 2.8 million solar-elevation-matched training sequences spanning latitudes, seasons, and water types. A compact two-stage transformer reconstructs the solar-elevation curve and predicts geolocation. Under leave-one-site-out tests, the site averaged median geodesic error is 500km--about an eightfold improvement over previous deep-learning baselines; with limited target-site data, the median error contracts to single-digit kilometers.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:e18d9bec114a4c24d0ee32b4c19bfb4dCVF · CVPR 2026

Towards Knowledge-augmented Bayesian Deep Learning For Computer Vision

Ma, Wang, Wang, Hanjing, Zhang, Yufei, Udayanga, Darsha, Ji, Qiang

原文摘要

Bayesian deep learning (BDL) integrates Bayesian inference with deep learning, improving predictive performance while enabling principled uncertainty quantification. However, existing BDLs often rely on non-informative random priors, limiting the benefits of Bayesian inference. In contrast, knowledge-augmented deep learning explicitly injects domain knowledge during training, yet lacks a probabilistic foundation. In this paper, we propose a knowledge-augmented BDL framework that integrates domain knowledge both as an informative prior and as an adaptive likelihood under a unified two-stage hybrid formulation. In the first stage, we learn a knowledge-informed prior p(\theta \mid \mathcal K ) by pre-training a model to satisfy domain-specific constraints. In the second stage, we perform Bayesian inference on task data with an adaptive knowledge likelihood p(\mathcal K \mid \theta, \mathcal D ), which dynamically enforces these constraints during optimization. This unified framework enables knowledge to guide both initialization and training, significantly improving prediction accuracy, robustness, adaptation and uncertainty estimation. Experiments on various computer vision tasks, including semi-synthetic and real-knowledge scenarios, demonstrate that our two-stage framework consistently outperforms state-of-the-art Bayesian and knowledge-augmented baselines.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

查看论文
CVF · CVPR 2026 ↗PDF ↗