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首次观测 · 记录 #1CVF · CVPR 2026:cvf:a38854ef306ec3b2367017846cbe570fCVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

DiT360: High-Fidelity Panoramic Image Generation via Hybrid Training

Feng, Haoran, Zhang, Dizhe, Li, Xiangtai, Du, Bo, Qi, Lu

原文摘要

In this work, we propose DiT360, a DiT-based framework that performs hybrid training on perspective and panoramic data for panoramic image generation. We attribute the main challenges in preserving geometric fidelity and photorealism to the scarcity of large-scale, high-quality real-world panoramic data, in contrast to prior methods that emphasize model design. Basically, DiT360 has several key modules for inter-domain transformation and intra-domain augmentation, applied at both the pre-VAE image level and the post-VAE token level. At the image level, we incorporate cross-domain knowledge through perspective image guidance and panoramic refinement, which enhance perceptual quality while regularizing diversity and photorealism. At the token level, hybrid supervision is applied across multiple modules, which include circular padding for boundary continuity, yaw loss for rotational robustness, and cube loss for distortion awareness. Extensive experiments on text-to-panorama, inpainting, and outpainting tasks demonstrate that our method achieves better boundary consistency and image fidelity across eleven quantitative metrics. Our code is available at https://github.com/Insta360-Research-Team/DiT360.

cs.CV

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:a38b6a9fcaba4f52d3bce394083db7f0CVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

$\phi$-DPO: Fairness Direct Preference Optimization Approach to Continual Learning in Large Multimodal Models

Truong, Thanh-Dat, Tran, Huu-Thien, Cothren, Jackson, Raj, Bhiksha, Luu, Khoa

原文摘要

Fairness in Continual Learning for Large Multimodal Models (LMMs) is an emerging yet underexplored challenge, particularly in the presence of imbalanced data distributions that can lead to biased model updates and suboptimal performance across tasks. While recent continual learning studies have made progress in addressing catastrophic forgetting, the problem of fairness caused by imbalanced data remains largely underexplored. This paper presents a novel Fairness Direct Preference Optimization (FaiDPO or \phi-DPO) framework for continual learning in LMMs. In particular, we first propose a new continual learning paradigm based on Direct Preference Optimization (DPO) to mitigate catastrophic forgetting by aligning learning with pairwise preference signals. Then, we identify the limitations of conventional DPO in imbalanced data and present a new \phi-DPO loss that explicitly addresses distributional biases. We provide a comprehensive theoretical analysis demonstrating that our approach addresses both forgetting and data imbalance. Additionally, to enable \phi-DPO-based continual learning, we construct pairwise preference annotations for existing benchmarks in the context of continual learning. Extensive experiments and ablation studies show the proposed \phi-DPO achieves State-of-the-Art performance across multiple benchmarks, outperforming prior continual learning methods of LMMs.

cs.CV

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:a398fa6e892fe5a6da21a56eda3117b3CVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

OmniFM: Toward Modality-Robust and Task-Agnostic Federated Learning for Heterogeneous Medical Imaging

Liu, Meilin, Wang, Jiaying, Shan, Jing

原文摘要

Federated learning (FL) has become a promising paradigm for collaborative medical image analysis, yet existing frameworks remain tightly coupled to task-specific backbones and are fragile under heterogeneous imaging modalities. Such constraints hinder real-world deployment, where institutions vary widely in modality distributions and must support diverse downstream tasks. To address this limitation, we propose OmniFM,a modality- and task-agnostic FL framework that unifies training across classification,segmentation, super-resolution, visual question answering,and multimodal fusion without re-engineering the optimization pipeline. OmniFM builds on a key frequency-domain insight: low-frequency spectral components exhibit strong cross-modality consistency and encode modality-invariant anatomical structures. Accordingly, OmniFM integrates(i) Global Spectral Knowledge Retrieval to inject global frequency priors, (ii) Embedding-wise Cross-Attention Fusion to align representations, and (iii) Prefix-Suffix Spectral Prompting to jointly condition global and personalized cues, together regularized by a Spectral-Proximal Alignment objective that stabilizes aggregation. Experiments on real-world datasets show that OmniFM consistently surpasses state-of-the-art FL baselines across intra- and cross-modality heterogeneity, achieving superior results under both fine-tuning and training-from-scratch setups.

cs.CV

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:a3a885b1a36ccfdb2e3c1358ed9ea87aCVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

MaskAdapt: Learning Flexible Motion Adaptation via Mask-Invariant Prior for Physics-Based Characters

Park, Soomin, Lee, Eunseong, Bin Lee, Kwang, Lee, Sung-Hee

原文摘要

We present MaskAdapt, a framework for flexible motion adaptation in physics-based humanoid control. The framework follows a two-stage residual learning paradigm. In the first stage, we train a mask-invariant base policy using stochastic body-part masking and a regularization term that enforces consistent action distributions across masking conditions. This yields a robust motion prior that remains stable under missing observations, anticipating later adaptation in those regions. In the second stage, a residual policy is trained atop the frozen base controller to modify only the targeted body parts while preserving the original behaviors elsewhere. We demonstrate the versatility of this design through two applications: (i) motion composition, where varying masks enable multi-part adaptation within a single sequence, and (ii) text-driven partial goal tracking, where designated body parts follow kinematic targets provided by a pre-trained text-conditioned autoregressive motion generator. Through experiments, MaskAdapt demonstrates strong robustness and adaptability, producing diverse behaviors under masked observations and delivering superior targeted motion adaptation compared to prior work.

cs.CV

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:a3ac94d421cfcd6de4b98ccf891325bcCVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

StyleTextGen: Style-Conditioned Multilingual Scene Text Generation

Chen, Zeyu, Zhao, Fangmin, Shu, Yan, Liu, Yichao, Yu, Liu, Zhou, Yu

原文摘要

Style-conditioned scene text generation faces unique challenges in extracting precise text styles from complex backgrounds and maintaining fine-grained style consistency across characters, especially for multilingual scripts. We propose StyleTextGen, a novel framework that learns to perceive and replicate visual text styles across different languages and writing systems. Our approach features three key contributions: First, we introduce a dual-branch style encoder dedicated to style modeling, yielding robust multilingual text style representations in complex real-world scenes. Second, we design a text style consistency loss that enhances style coherence and improves overall visual quality. Third, we develop a mask-guided inference strategy that ensures precise style alignment between generated and reference text. To facilitate systematic evaluation, we construct StyleText-CE, a bilingual scene text style benchmark covering both monolingual and cross-lingual settings. Extensive experiments demonstrate that StyleTextGen significantly outperforms existing methods in style consistency and cross-lingual generalization, establishing new state-of-the-art performance in multilingual style-conditioned text generation.

cs.CV

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:a3ccd55e3cd4c300adef64b4983c280dCVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

MoDES: Accelerating Mixture-of-Experts Multimodal Large Language Models via Dynamic Expert Skipping

Huang, Yushi, Wang, Zining, Yuan, Zhihang, Ding, Yifu, Gong, Ruihao, Guo, Jinyang, Liu, Xianglong, Zhang, Jun

原文摘要

Mixture-of-Experts (MoE) Multimodal large language models (MLLMs) excel at vision-language tasks, but they suffer from high computational inefficiency. To reduce inference overhead, expert skipping methods have been proposed to deactivate redundant experts based on the current input tokens. However, we find that applying these methods--originally designed for unimodal large language models (LLMs)--to MLLMs results in considerable performance degradation. This is primarily because such methods fail to account for the heterogeneous contributions of experts across MoE layers and modality-specific behaviors of tokens within these layers. Motivated by these findings, we propose MoDES, the first training-free framework that adaptively skips experts to enable efficient and accurate MoE MLLM inference. It incorporates a globally-modulated local gating (GMLG) mechanism that integrates global layer-wise importance into local routing probabilities to accurately estimate per-token expert importance. A dual-modality thresholding (DMT) method is then applied, which processes tokens from each modality separately, to derive the skipping schedule. To set the optimal thresholds, we introduce a frontier search algorithm that exploits monotonicity properties, cutting convergence time from several days to a few hours. Extensive experiments for 3 model series across 13 benchmarks demonstrate that MoDES far outperforms previous approaches. For instance, when skipping 88% experts for Qwen3-VL-MoE-30B-A3B-Instruct, the performance boost is up to **10.67%** (97.33% vs. 86.66%). Furthermore, MoDES significantly enhances inference speed, improving the prefilling time by **2.16x** and the decoding time by **1.26x**.

cs.CV · cs.CL

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:a3e766bc4b6f906415c0c67f80f59fc6CVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

AXG-Reasoner: Error Detection and Explanation in Long Task Videos with Vision-Language Models

Lee, Shih-Po, Elhamifar, Ehsan

原文摘要

Virtual task assistants must recognize and explain users' mistakes to provide effective and corrective guidance. In this paper, we address the problem of error reasoning in long task videos, which is to detect and explain errors. Although recent Vision-Language Models (VLMs) demonstrate strong capabilities in visual question answering, they struggle to attend to the sparse spatiotemporal cues associated with errors in long task videos. We introduce an error reasoning framework, AXG-Reasoner, that leverages a frozen VLM in conjunction with a proposed Action eXecution Graph (AXG) and a temporal action segmentation (TAS) model, obtained and learned from normal (error-free) videos. To enable VLMs to attend to the sparse spatiotemporal cues associated with errors, we decompose each action segment of the video, obtained by TAS, into a sequence of fine-grained subactions by aligning it with the AXG. For each subaction segment, we query the VLM using a small number of keyframes and enhanced prompts to detect and explain errors, enabling data efficiency. To avoid costly manual subaction annotations, we develop a method to automatically construct AXG from training videos using foundation models. Extensive experiments on EgoPER and CaptainCook4D show that our method consistently improves over VLM baselines in error explanation by effectively identifying spatiotemporal cues and achieves state-of-the-art performance in error detection.

cs.CV · cs.CL

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:a3fc74a1a70aa8f7a8e2592c6f3d7f44CVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

Online Data Curation for Object Detection via Marginal Contributions to Dataset-level Average Precision

Sun, Zitang, Yoshimura, Masakazu, Otsuka, Junji, Irie, Atsushi, Ohashi, Takeshi

原文摘要

High-quality data has become a primary driver of progress under scale laws, with curated datasets often outperforming much larger unfiltered ones at lower cost. Online data curation extends this idea by dynamically selecting training samples based on the model's evolving state. While effective in classification and multimodal learning, existing online sampling strategies rarely extend to object detection because of its structural complexity and domain gaps. We introduce DetGain, an online data curation method specifically for object detection that estimates the marginal perturbation of each image to dataset-level Average Precision (AP) based on its prediction quality. By modeling global score distributions, DetGain efficiently estimates the global AP change and computes teacher-student contribution gaps to select informative samples at each iteration. The method is architecture-agnostic and minimally intrusive, enabling straightforward integration into diverse object detection architectures. Experiments on the COCO dataset with multiple representative detectors show consistent improvements in accuracy. DetGain also demonstrates strong robustness under low-quality data and can be effectively combined with knowledge distillation techniques to further enhance performance, highlighting its potential as a general and complementary strategy for data-efficient object detection.

cs.CV

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:a404c45f3bf68ba18132f5cbb0683e09CVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

Rethinking MLLM Itself as a Segmenter with a Single Segmentation Token

Zhang, Anqi, Ji, Xiaokang, Gao, Guangyu, Jiao, Jianbo, Liu, Chi Harold, Wei, Yunchao

原文摘要

Recent segmentation methods leveraging Multi-modal Large Language Models (MLLMs) have shown reliable object-level segmentation and enhanced spatial perception. However, almost all previous methods predominantly rely on specialist mask decoders to interpret masks from generated segmentation-related embeddings and visual features, or incorporate multiple additional tokens to assist. This paper aims to investigate whether and how we can unlock segmentation from MLLM itSELF with 1 segmentation Embedding (SELF1E) while achieving competitive results, which eliminates the need for external decoders. To this end, our approach targets the fundamental limitation of resolution reduction in pixel-shuffled image features from MLLMs. First, we retain image features at their original uncompressed resolution, and refill them with residual features extracted from MLLM-processed compressed features, thereby improving feature precision. Subsequently, we integrate pixel-unshuffle operations on image features with and without LLM processing, respectively, to unleash the details of compressed features and amplify the residual features under uncompressed resolution, which further enhances the resolution of refilled features. Moreover, we redesign the attention mask with dual perception pathways, i.e., image-to-image and image-to-segmentation, enabling rich feature interaction between pixels and the segmentation token. Comprehensive experiments across multiple segmentation tasks validate that SELF1E achieves performance competitive with specialist mask decoder-based methods, demonstrating the feasibility of decoder-free segmentation in MLLMs. Project page: https://github.com/ANDYZAQ/SELF1E.

cs.CV · cs.CL

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:a4084d8aeca3875dab25e4ff64553925CVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

IEBGL:An Interpretability-Enhanced Brain Graph Learning Framework with LLM-Instructed Topology and Literature-Augmented Semantics

Duan, Yihang, Huang, Shuo, Zhang, Li, Wang, Meiling, Zhang, Li

原文摘要

Resting-state functional MRI (rs-fMRI) provides rich information for modeling brain connectivity in disease diagnosis. However, most existing brain graph learning methods rely solely on imaging data, leading to limited biological interpretability and poor integration of external medical knowledge. To address these challenges, we propose an Interpretability-Enhanced Brain Graph Learning (IEBGL) framework that anchors brain network modeling in large-scale medical knowledge. Our framework introduces two complementary modules: LLM-Instructed Topological Reconstruction (LITR) and Literature-Augmented Semantic Aggregation (LASA). LITR employs large language model (LLM) reasoning to refine brain connectivity and construct topological structure. LASA augments node representations by aggregating semantic information from biomedical literature, ensuring the model's interpretability and relevance to clinical disease knowledge. Finally, the framework is trained with the Graph Bi-directional Mamba Network (GBMN) for disease diagnosis. Extensive experiments on the REST-meta-MDD and ABIDE datasets, together with 35,133 depression-related and 32,617 autism-related publications, demonstrate that IEBGL outperforms state-of-the-art methods in classification performance. Further analyses show that the LITR module reveals biologically meaningful alterations in brain connectivity, while the LASA module establishes interpretable associations between these regions and disease-related biomedical literature. Together, these mechanisms help IEBGL explain abnormal brain connections and their links to disease-related knowledge.

cs.CV · cs.CL

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:a4283c92326b81f18b93c8a5489296d7CVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

D-Prism: Differentiable Primitives for Structured Dynamic Modeling

Yu, Xingyuan, Li, Yijin, Zeng, Chong, Ming, Yuhang, Bao, Hujun, Zhang, Guofeng

原文摘要

Capturing both geometry and rigid motion for structured dynamic objects, like multi-part assemblies or jointed mechanisms, remains a key challenge. Existing dynamic methods, such as deformable meshes or 3DGS, rely on unstructured representations and fail to jointly model suitable geometry and articulated motion. Primitive-based methods excel at structured static scenes, but their dynamic potential is still unexplored. We propose D-Prism, the first framework to achieve high-fidelity structured dynamic modeling by extending differentiable primitives to the dynamic domain.Specifically, we bind 3DGS to primitive surfaces, leveraging their respective strengths in appearance and geometry. We introduce a deformation network to control primitive motion, ensuring it accurately matches the object's movement. Furthermore, we design a novel adaptive control strategy to dynamically adjust primitive counts, better matching objects' true spatial footprint.Experiments confirm that our method excels at structured dynamic modeling, providing both structured geometry and precise motion tracking. Project page: https://zju3dv.github.io/d-prism/.

cs.CV

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:a42aea1301c7406e75a7916939306934CVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

RHCNet: Residual-Guided Hierarchical Calibration Network for Robust Underwater Object Detection

Wang, Yueying, Guo, Yiteng, Zhang, Weidong, Wen, Jie, Shen, Liquan, Yan, Huaicheng, Xu, Xin

原文摘要

Underwater images commonly suffer from foreground-background ambiguity, loss of structural details, and severely reduced contrast, which collectively make underwater object detection (UOD) an inherently challenging task. To handle this issue, we present a residual-guided hierarchical calibration network (RHCNet) designed to achieve more efficient and robust UOD, which comprises a residual-guided feature enhancement module (RGFE) and a hierarchical feature calibration pyramid module (HFCP). Concretely, RHCNet extends the standard ResNet-50 backbone by embedding the RGFE, which effectively strengthens the representation of edge and texture features in blurry regions by jointly leveraging convolutional operations and attention mechanisms to achieve more discriminative feature extraction for UOD. Subsequently, the HFCP integrates a bottom-up semantic enhancement path and a top-down fine-grained feature compensation path, while a K-means clustering-guided feature calibration module is jointly employed to ensure multi-level cross-scale semantic consistency and accurate alignment of salient region features. Extensive experiments on the DUO and UTDAC benchmark datasets demonstrated that our RHCNet attains the highest AP scores of 70.53% and 53.35%, respectively. Besides, our RHCNet also maintains excellent detection accuracy and strong generalization capability on the COCO dataset for terrestrial scenarios. The code is available at https://github.com/YitengGuo/RHCNet.

cs.CV

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:a4533a79d1a62ba12eafcc36304c3794CVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

VerseCrafter: Dynamic Realistic Video World Model with 4D Geometric Control

Zheng, Sixiao, Yin, Minghao, Hu, Wenbo, Li, Xiaoyu, Shan, Ying, Fu, Yanwei

原文摘要

Video world models aim to simulate dynamic, real-world environments, yet existing methods struggle to provide unified and precise control over camera and multi-object motion, as videos inherently capture dynamics in the projected 2D image plane. To bridge this gap, we introduce VerseCrafter, a geometry-driven video world model that generates dynamic, realistic videos from a unified 4D geometric world state. Our approach is centered on a novel 4D Geometric Control representation, which encodes the world state as a static background point cloud and per-object 3D Gaussian trajectories. This representation captures each object's motion path and probabilistic 3D occupancy over time, providing a flexible, category-agnostic alternative to rigid bounding boxes and parametric models. We render 4D Geometric Control into 4D control maps for a pretrained video diffusion model, enabling high-fidelity, view-consistent video generation that faithfully follows the specified dynamics. To enable training at scale, we develop an automatic data engine and construct VerseControl4D, a real-world dataset of 35K training samples with automatically derived prompts and rendered 4D control maps. Extensive experiments show that VerseCrafter achieves superior visual quality and more accurate control over camera and multi-object motion than prior methods.

cs.CV

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:a49563e6d339a13402b16e0c04767ef2CVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

ReFlow: Self-correction Motion Learning for Dynamic Scene Reconstruction

Liang, Yanzhe, Zhu, Ruijie, Chang, Hanzhi, Li, Zhuoyuan, Lu, Jiahao, Zhang, Tianzhu

原文摘要

We present ReFlow, a unified framework for monocular dynamic scene reconstruction that learns 3D motion in a novel self-correction manner from raw video. Existing methods often suffer from incomplete scene initialization for dynamic regions, leading to unstable reconstruction and motion estimation, which often resorts to external dense motion guidance such as pre-computed optical flow to further stabilize and constrain the reconstruction of dynamic components. However, this introduces additional complexity and potential error propagation.To address these issues, ReFlow integrates a Complete Canonical Space Construction module for enhanced initialization of both static and dynamic regions, and a Separation-Based Dynamic Scene Modeling module that decouples static and dynamic components for targeted motion supervision.The core of ReFlow is a novel self-correction flow matching mechanism, consisting of Full Flow Matching to align 3D scene flow with time-varying 2D observations, and Camera Flow Matching to enforce multi-view consistency for static objects. Together, these modules enable robust and accurate dynamic scene reconstruction.Extensive experiments across diverse scenarios demonstrate that ReFlow achieves superior reconstruction quality and robustness, establishing a novel self-correction paradigm for monocular 4D reconstruction.

cs.CV

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:a499455afa217859e1f28c82cb5e6f21CVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

HQC-NBV: A Hybrid Quantum-Classical View Planning Approach

Yu, Xiaotong, Chen, Chang Wen

原文摘要

Efficient view planning is a fundamental challenge in computer vision and robotic perception, critical for tasks ranging from search and rescue operations to autonomous navigation. While classical approaches, including sampling-based and deterministic methods, have shown promise in planning camera viewpoints for scene exploration, they often struggle with computational scalability and solution optimality in complex settings. This study introduces HQC-NBV, a hybrid quantum-classical framework for view planning that leverages quantum properties to efficiently explore the parameter space while maintaining robustness and scalability. We propose a specific Hamiltonian formulation with multi-component cost terms and a parameter-centric variational ansatz with bidirectional alternating entanglement patterns that capture the hierarchical dependencies between viewpoint parameters. Comprehensive experiments demonstrate that quantum-specific components provide measurable performance advantages. Compared to the classical methods, our approach achieves 7.9-49.2% higher exploration efficiency across diverse environments. Our analysis of entanglement architecture and coherence-preserving terms provides insights into the mechanisms of quantum advantage in robotic exploration tasks. This work represents a significant advancement in integrating quantum computing into robotic perception systems, offering a paradigm-shifting solution for various robot vision tasks.

cs.CV

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:a4a608a9abe21ae534838bdf6c34e42bCVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

Beyond Top Activations: Efficient and Reliable Crowdsourced Evaluation of Automated Interpretability

Oikarinen, Tuomas, Yan, Ge, Kulkarni, Akshay, Weng, Tsui-Wei

原文摘要

Interpreting individual neurons or directions in activation space is an important topic in mechanistic interpretability. Numerous automated interpretability methods have been proposed to generate such explanations, but it remains unclear how reliable these explanations are, and which methods produce the most accurate descriptions. While crowd-sourced evaluations are commonly used, existing pipelines are noisy, costly, and typically assess only the highest-activating inputs, leading to unreliable results. In this paper, we introduce two techniques to enable cost-effective and accurate crowdsourced evaluation of automated interpretability methods beyond top activating inputs. First, we propose Model-Guided Importance Sampling (MG-IS) to select the most informative inputs to show human raters. In our experiments, we show this reduces the number of inputs needed to reach the same evaluation accuracy by 13x. Second, we address label noise in crowd-sourced ratings through Bayesian Rating Aggregation (BRAgg), which allows us to reduce the number of ratings per input required to overcome noise by 3x. Together, these techniques reduce the evaluation cost by 40x, making large-scale evaluation feasible. Finally, we use our methods to conduct a large scale crowd-sourced study comparing recent automated interpretability methods for vision networks.

cs.CV

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:a4a9f28f9249e735890b211f0698badeCVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

The Road Less Seen: Segment Exploration for Weakly Supervised Video Anomaly Detection

Acharya, Anusha, Sapkota, Hitesh, Yu, Qi, Liu, Xumin

原文摘要

Weakly supervised learning (WSL) provides a cost-effective learning paradigm for video anomaly detection (VAD) from data with video-level annotation instead of requiring costly fine-grained segment-level annotation. Although contemporary methods have shown promising results on challenging real-world surveillance videos, most of them are evaluated using the Area Under the Receiver Operating Characteristic Curve (AUROC). We reveal that a high AUROC could result in a very low recall for meaningful False Positive Rate (FPR) thresholds. Thus, these models suffer from limited practical values, especially in high-stake domains (e.g. public safety and medical diagnosis), where missing the true anomalies incur high cost. This surprising phenomenon is rooted in the interplay of weak supervision and the highly imbalanced distribution between normal and anomalous video segments. To tackle this key challenge in VAD systems, we propose a novel dual exploration strategy that combines temporal clustering with uncertainty-based segment exploration. Temporal clustering selects diverse segments based on both semantic and temporal similarity, while uncertainty-based sampling targets low-scoring segments with high model uncertainty. The main aim of exploration is to ensure that the model learns from a wide range of patterns, both diverse and ambiguous, resulting in more informed and robust decision-making, and reduction in false negatives. Meanwhile, we adopt two practical metrics to replace the commonly used AUROC score for a more effective measure for evaluation. Experiments conducted in challenging real-world videos demonstrate that our exploration strategy improves VAD performance compared to the baselines on these metrics, which justifies its improved practical value in real-world settings.

cs.CV

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

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来源日期 2026-01-01（仅到年份）

DetectSCI: Toward Object-Guided ROI Reconstruction for High-Resolution Video Snapshot Compressive Imaging

Jiang, Xingjian, Wang, Lishun, Wang, Ping, Yuan, Xin

原文摘要

Video snapshot compressive imaging (SCI) offers a promising alternative to high-speed cameras by encoding multiple frames into a single 2D measurement. However, SCI requires algorithms to reconstruct the high-speed video, and as resolution increases, reconstruction becomes computationally expensive and memory-intensive. Much of the resource is wasted on recovering large background regions that contain little useful information, highlighting the need for selective, object-driven reconstruction. Existing object detectors struggle to perform accurately on SCI measurements due to the spatial-temporal aliasing introduced by coded exposure. To address this challenge, we propose DetectSCI, the first framework enabling object-guided region-of-interest (ROI) reconstruction for high-resolution SCI. The inside detector comprises two key components: an encoder built from weight-sharing Mamba-Implicit Modules (MIM) for progressive feature refinement, and a Frequency Mamba (FM) module dedicated to frequency-aware query selection. MIM enhances features via multi-scale dilated convolutions and implicit representations, while FM restores discriminative details by decomposing and reweighting frequency bands. Experiments on the SportsMOT dataset show that DetectSCI achieves 80.9 Average Precision (AP), surpassing the best CNN-based detector by at least 2.8 AP and the best Transformer-based detector by at least 4.1 AP, while maintaining comparable efficiency. Code will be released.

cs.CV

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

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来源日期 2026-01-01（仅到年份）

UniCompress: Token Compression for Unified Vision-Language Understanding and Generation

Wang, Ziyao, Chen, Chen, Li, Jingtao, Zhuang, Weiming, Huang, Jiabo, Li, Ang, Lyu, Lingjuan

原文摘要

Unified models aim to support both understanding and generation by encoding images into discrete tokens and processing them alongside text within a single autoregressive framework. This unified design offers architectural simplicity and cross-modal synergy, which facilitates shared parameterization, consistent training objectives, and seamless transfer between modalities. However, the large number of visual tokens required by such models introduces substantial computation and memory overhead, and this inefficiency directly hinders deployment in resource constrained scenarios such as embodied AI systems. In this work, we propose a unified token compression algorithm UniCompress that significantly reduces visual token count while preserving performance on both image understanding and generation tasks. Our method introduces a plug-in compression and decompression mechanism guided with learnable global meta tokens. The framework is lightweight and modular, enabling efficient integration into existing models without full retraining. Experimental results show that our approach reduces image tokens by up to (4x), achieves substantial gains in inference latency and training cost, and incurs only minimal performance degradation, which demonstrates the promise of token-efficient unified modeling for real world multimodal applications.

cs.CV

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来源日期 2026-01-01（仅到年份）

CADC: Content Adaptive Diffusion-Based Generative Image Compression

Sheng, Xihua, Zhu, Lingyu, Zhang, Tianyu, Liu, Dong, Wang, Shiqi, Wang, Jing

原文摘要

Diffusion-based generative image compression has demonstrated remarkable potential for achieving realistic reconstruction at ultra-low bitrates. The key to unlocking this potential lies in making the entire compression process content-adaptive, ensuring that the encoder's representation and the decoder's generative prior are dynamically aligned with the semantic and structural characteristics of the input image. However, existing methods suffer from three critical limitations that prevent effective content adaptation. First, isotropic quantization applies a uniform quantization step, failing to adapt to the spatially varying complexity of image content and creating a misalignment with the diffusion model's noise-dependent prior. Second, the information concentration bottleneck---arising from the dimensional mismatch between the high-dimensional noisy latent and the diffusion decoder's fixed input---prevents the model from adaptively preserving essential semantic information in the primary channels. Third, existing textual conditioning strategies either need significant textual bitrate overhead or rely on generic, content-agnostic textual prompts, thereby failing to provide adaptive semantic guidance efficiently. To overcome these limitations, we propose a content-adaptive diffusion-based image codec (CADC) with three technical innovations: 1) an Uncertainty-Guided Adaptive Quantization (UGAQ) method that learns spatial uncertainty maps to adaptively align quantization distortion with content characteristics; 2) an Auxiliary Decoder-Guided Information Concentration (ADGIC) method that uses a lightweight auxiliary decoder to enforce content-aware information preservation in the primary latent channels; and 3) a Bitrate-Free Adaptive Textual Conditioning (BFATC) method that derives content-aware textual descriptions from the auxiliary reconstructed image, enabling semantic guidance without bitrate cost. Comprehensive experimental results show that our codec achieves state-of-the-art perceptual quality at ultra-low bitrates.

cs.CV

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来源日期 2026-01-01（仅到年份）

Ground Reaction Inertial Poser: Physics-based Human Motion Capture from Sparse IMUs and Insole Pressure Sensors

Hori, Ryosuke, Song, Jyun-Ting, Luo, Zhengyi, Cao, Jinkun, Shin, Soyong, Saito, Hideo, Kitani, Kris

原文摘要

We propose Ground Reaction Inertial Poser (GRIP), a method that reconstructs physically plausible human motion using four wearable devices. Unlike conventional IMU-only approaches, GRIP combines IMU signals with foot pressure data to capture both body dynamics and ground interactions. Furthermore, rather than relying solely on kinematic estimation, GRIP uses a digital twin of a person, in the form of a synthetic humanoid in a physics simulator, to reconstruct realistic and physically plausible motion. At its core, GRIP consists of two modules: KinematicsNet, which estimates body poses and velocities from sensor data, and DynamicsNet, which controls the humanoid in the simulator using the residual between the KinematicsNet prediction and the simulated humanoid state. To enable robust training and fair evaluation, we introduce a large-scale dataset, Pressure and Inertial Sensing for Human Motion and Interaction (PRISM), that captures diverse human motions with synchronized IMUs and insole pressure sensors. Experimental results show that GRIP outperforms existing IMU-only and IMU-pressure fusion methods across all evaluated datasets, achieving higher global pose accuracy and improved physical consistency.

cs.CV

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来源日期 2026-01-01（仅到年份）

A2GC: Asymmetric Aggregation with Geometric Constraints for Locally Aggregated Descriptors

Li, Zhenyu, Shang, Tianyi

原文摘要

Visual Place Recognition (VPR) aims to match query images against a database using visual cues. State-of-the-art methods aggregate features from deep backbones to form global descriptors. Optimal transport-based aggregation methods reformulate feature-to-cluster assignment as a transport problem, but the standard Sinkhorn algorithm symmetrically treats source and target marginals, limiting effectiveness when image features and cluster centers exhibit substantially different distributions. We propose an asymmetric aggregation VPR method with geometric constraints for locally aggregated descriptors, called A2GC-VPR. Our method employs row-column normalization averaging with separate marginal calibration, enabling asymmetric matching that adapts to distributional discrepancies in visual place recognition. Geometric constraints are incorporated through learnable coordinate embeddings, computing compatibility scores fused with feature similarities, thereby promoting spatially proximal features to the same cluster and enhancing spatial awareness. Experimental results on MSLS, NordLand, and Pittsburgh datasets demonstrate superior performance, validating the effectiveness of our approach in improving matching accuracy and robustness. Our code is available at https://github.com/CV4RA/A2GC.

cs.CV

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来源日期 2026-01-01（仅到年份）

KASALv2: Fully Automatic 3D Rotational Symmetry Classification and Axis Localization

Zhang, Mengxin, Wang, Yulin, Luo, Chen, Li, Yongzhe, Zhou, Yijun

原文摘要

Rotational symmetry is an important prior in 6D pose estimation, improving pose accuracy and supporting symmetry-aware evaluation. However, current symmetry annotations for 3D objects remain largely manual or semi-automatic, often requiring predefined types or orders, which limits scalability. This work introduces a fully automatic, reference-free framework for symmetry-type classification, rotational-order identification, and full-axis localization across all eight canonical 3D rotational symmetry types. The method localizes a dominant high-order axis, infers its rotational order through self-consistency analysis, and reconstructs the complete symmetry structure under a hierarchy-guided formulation. A texture-aware extension further models appearance-induced reductions in rotational order while preserving axis orientations. Experiments on idealized and real-world datasets demonstrate strong accuracy and generalization, achieving 94.75% accuracy on 438 symmetric objects in GSO. Training FoundationPose with these priors improves accuracy by up to 0.9% across five BOP datasets, showing that automatically estimated rotational priors improve downstream 6D pose estimation. Code is available at https://github.com/WangYuLin-SEU/KASAL.

cs.CV

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来源日期 2026-01-01（仅到年份）

AirSim360: A Panoramic Simulation Platform within Drone View

Ge, Xian, Pan, Yuling, Zhang, Yuhang, Li, Xiang, Zhang, Weijun, Zhang, Dizhe, Wan, Zhaoliang, Lin, Xin 等 15 位作者

原文摘要

The field of 360-degree omnidirectional understanding has been receiving increasing attention for advancing spatial intelligence. However, the lack of large-scale and diverse data remains a major limitation. In this work, we propose AirSim360, a simulation platform for omnidirectional data from aerial viewpoints, enabling wide-ranging scene sampling with drones. Specifically, AirSim360 focuses on three key aspects: a render-aligned data and labeling paradigm for pixel-level geometric, semantic, and instance-level understanding; an interactive pedestrian-aware system for modeling human behavior; and an automated trajectory generation paradigm to support navigation tasks. Furthermore, we collect more than 60K panoramic samples and conduct extensive experiments across various tasks to demonstrate the effectiveness of our simulator. Unlike existing simulators, our work is the first to systematically model the 4D real world under an omnidirectional setting. The entire platform, including the toolkit, plugins, and collected datasets, will be made publicly available.

cs.CV

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来源日期 2026-01-01（仅到年份）

Unleashing Stealthy Backdoor Pandemic by Infecting a Single Diffusion Model

Al Nahian, Mohaiminul, Almalky, Abeer Matar, Ahmed, Sabbir, Al Arafat, Abdullah, Rizve, Mamshad Nayeem, Rakin, Adnan Siraj

原文摘要

The remarkable success of modern Deep Neural Networks (DNNs) can be primarily attributed to having access to compute resources and high-quality labeled data, which is often costly and challenging to acquire. Recently, text-to-image Diffusion Models (DMs) have emerged as powerful data generators to augment training datasets. Machine learning practitioners often utilize off-the-shelf third-party DMs for generating synthetic data without domain-specific expertise or adaptation. Such a practice leads to a novel and insidious threat: a diffusion model infected with a backdoor can effectively spread into a large number of downstream models, causing a backdoor pandemic. To achieve this for the first time, we propose Eidolon, designed and optimized to stealthily transfer the backdoor injected into a single diffusion model into virtually an unlimited number of downstream models without any active attacker role in the downstream training tasks. Proposed Eidolon not only makes the attack stealthier and effective, but it also enforces a strict threat model for injecting a backdoor into the downstream model compared to conventional backdoor attacks. We propose four necessary tests that a successful backdoor attack on the diffusion model should pass to cause a backdoor pandemic. Our evaluation across a wide range of benchmark datasets and model architectures exhibits that only our attack successfully passes these tests, causing widespread pandemic across many downstream models. Code is available at https://github.com/ML-Security-Research-LAB/Eidolon

cs.CV

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来源日期 2026-01-01（仅到年份）

Restore, Assess, Repeat: A Unified Framework for Iterative Image Restoration

Chen, I-Hsiang, Hadji, Isma, Sanchez, Enrique, Bulat, Adrian, Kuo, Sy-Yen, Timofte, Radu, Tzimiropoulos, Georgios, Martinez, Brais

原文摘要

Image restoration aims to recover high quality images from inputs degraded by various factors, such as adverse weather, blur, or low light. While recent studies have shown remarkable progress across individual or unified restoration tasks, they still suffer from limited generalization and inefficiency when handling unknown or composite degradations. To address these limitations, we propose RAR, a Restore, Assess and Repeat process, that integrates Image Quality Assessment (IQA) and Image Restoration (IR) into a unified framework to iteratively and efficiently achieve high quality image restoration. Specifically, we introduce a restoration process that operates entirely in the latent domain to jointly perform degradation identification, image restoration, and quality verification. The resulting model is fully trainable end to end and allows for an all-in-one assess and restore approach that dynamically adapts the restoration process. Also, the tight integration of IQA and IR into a unified model minimizes the latency and information loss that typically arises from keeping the two modules disjoint, (e.g. during image and/or text decoding). Extensive experiments show that our approach consistent improvements under single, unknown and composite degradations, thereby establishing a new state-of-the-art.

cs.CV

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来源日期 2026-01-01（仅到年份）

When Local Rules Create Global Order: Self-Organized Representation Learning for Latent Diffusion Models

Lian, Junrong, Deng, Weijian, Wei, Pengxu, Chen, Yaqin, Ye, Qixiang, Lin, Liang

原文摘要

This work studies how latent space structure impacts the performance of Latent Diffusion Models (LDMs). We show that effective generation requires a latent space that is simultaneously locally smooth, enabling stable and reliable reconstruction, and globally dispersive, allowing the model to draw diverse and meaningful samples without collapsing into narrow regions of the latent space. However, existing approaches often emphasize smoothness, which may lead to concentrated latent regions and limited exploration of the broader space. To address these limitations, we propose Self-Organized Representation Learning (SORL), a bottom-up training paradigm inspired by self-organization in complex systems, where global structure emerges naturally from simple local interactions. The critical latent properties of smoothness and maximal dispersity are not explicitly imposed. Instead, SORL promotes these properties through two complementary local mechanisms: local attraction, which encourages coherent reconstructions among nearby latent codes, and local repulsion, which prevents latent codes from collapsing into dense clusters. Through their interaction, SORL induces a latent manifold that maintains both local smoothness and global dispersity, leading to improved reconstruction and generation.

cs.CV

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来源日期 2026-01-01（仅到年份）

WorldMM: Dynamic Multimodal Memory Agent for Long Video Reasoning

Yeo, Woongyeong, Kim, Kangsan, Yoon, Jaehong, Hwang, Sung Ju

原文摘要

Recent advances in video large language models have demonstrated strong capabilities in understanding short clips. However, scaling them to hours- or days-long videos remains highly challenging due to limited context capacity and the loss of critical visual details during abstraction. Existing memory-augmented methods mitigate this by leveraging textual summaries of video segments, yet they heavily rely on text and fail to utilize visual evidence when reasoning over complex scenes. Moreover, retrieving from fixed temporal scales further limits their flexibility in capturing events that span variable durations. To address this, we introduce WorldMM, a novel multimodal memory agent that constructs and retrieves from multiple complementary memories, encompassing both textual and visual representations. WorldMM comprises three types of memory: episodic memory indexes factual events across multiple temporal scales, semantic memory continuously updates high-level conceptual knowledge, and visual memory preserves detailed information about scenes. During inference, an adaptive retrieval agent iteratively selects the most relevant memory source and leverages multiple temporal granularities based on the query, continuing until it determines that sufficient information has been gathered. WorldMM significantly outperforms existing baselines across five long video question-answering benchmarks, achieving an average 8.4% performance gain over previous state-of-the-art methods, showing its effectiveness on long video reasoning.

cs.CV · cs.CL

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来源日期 2026-01-01（仅到年份）

CARD: Correlation Aware Restoration with Diffusion

Nezakati, Niki, Ghosh, Arnab, Roy-Chowdhury, Amit, Saragadam, Vishwanath

原文摘要

Denoising diffusion models have achieved state-of-the-art performance in image restoration by modeling the process as sequential denoising steps. However, most approaches assume independent and identically distributed (i.i.d.) Gaussian noise, while real-world sensors often exhibit spatially correlated noise due to readout mechanisms, limiting restoration quality in practice. We introduce Correlation Aware Restoration with Diffusion (CARD), a training-free extension of DDRM that explicitly handles correlated Gaussian noise. CARD first whitens the noisy observation, converting the noise into an i.i.d. form. The diffusion restoration steps are then replaced with noise-whitened updates, which inherit DDRM's closed-form sampling efficiency while enabling restoration under correlated noise. To emphasize the importance of addressing correlated noise, we contribute CIN-D, a novel correlated noise dataset captured across diverse illumination conditions to evaluate restoration methods on real rolling-shutter sensor noise. This dataset fills a critical gap in the literature for experimental evaluation with real-world correlated noise. Experiments on standard benchmarks with synthetic correlated noise and on CIN-D demonstrate that CARD consistently outperforms existing methods across denoising, deblurring, and super-resolution tasks.

cs.CV

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来源日期 2026-01-01（仅到年份）

Fed-ADE: Adaptive Learning Rate for Federated Post-adaptation under Distribution Shift

Park, Heewon, Joe, Mugon, Kim, Miru, Im, Kyungjin, Kwon, Minhae

原文摘要

Federated learning (FL) in post-deployment settings must adapt to non-stationary data streams across heterogeneous clients without access to ground-truth labels. A major challenge is learning rate selection under client-specific, time-varying distribution shifts, where fixed learning rates often lead to underfitting or divergence. We propose Fed-ADE (Federated Adaptation with Distribution Shift Estimation), an unsupervised federated adaptation framework that leverages lightweight estimators of distribution dynamics. Specifically, Fed-ADE employs uncertainty dynamics estimation to capture changes in predictive uncertainty and representation dynamics estimation to detect covariate-level feature drift, combining them into a per-client, per-timestep adaptive learning rate. We provide theoretical analyses showing that our dynamics estimation approximates the underlying distribution shift and yields dynamic regret and convergence guarantees. Experiments on image and text benchmarks under diverse distribution shifts (label, covariate, and concept) demonstrate consistent improvements over strong baselines. These results highlight that distribution shift-aware adaptation enables effective and robust federated post-adaptation under real-world non-stationarity.

cs.CV

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