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

+0新增论文
+0其中 AI 相关
+2已有论文修订
+100新增 AI 导读

arXiv 最近完成扫描：2026-11-08 15:33 · 全站最近入库：2026-11-08 06:02各信源进度 →

统计口径与数据新鲜度

统计 2026-11-07 16:34 — 2026-11-08 16:34（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：2026-11-08 15:33最近发现新增版本：2026-11-08 06:02库内论文最新更新时间：2026-11-06 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。

17,100 已收录论文14,661 AI 相关4,057 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 10 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-08 15:31（北京时间）
首次观测 · 记录 #1CVF · CVPR 2026:cvf:1c0b9b1dbd2a22a9b16a66dcf9025385CVF · CVPR 2026

Monocular Open Vocabulary Occupancy Prediction for Indoor Scenes

Zhou, Changqing, Luo, Yueru, Zhang, Han, Jiang, Zeyu, Chen, Changhao

原文摘要

Open-vocabulary 3D occupancy is vital for embodied agents, which need to understand complex indoor environments where semantic categories are abundant and evolve beyond fixed taxonomies. While recent work has explored open-vocabulary occupancy in outdoor driving scenarios, such methods transfer poorly indoors, where geometry is denser, layouts are more intricate, and semantics are far more fine-grained. To address these challenges, we adopt a geometry-only supervision paradigm that uses only binary occupancy labels (occupied vs. free). Our framework builds upon 3D Language-Embedded Gaussians, which serve as a unified intermediate representation coupling fine-grained 3D geometry with a language-aligned semantic embedding. On the geometry side, we find that existing Gaussian-to-Occupancy operators fail to converge under such weak supervision, and we introduce an opacity-aware, Poisson-based approach that stabilizes volumetric aggregation. On the semantic side, direct alignment between rendered features and open-vocabulary segmentation features suffers from feature mixing; we therefore propose a Progressive Temperature Decay schedule that gradually sharpens opacities during splatting, strengthening Gaussian-language alignment. On Occ-ScanNet, our framework achieves 59.50 IoU and 21.05 mIoU in the open-vocabulary setting, surpassing all existing occupancy methods in IoU and outperforming prior open-vocabulary approaches by a large margin in mIoU. Code will be released at https://github.com/JuIvyy/LegoOcc.

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:2469cd9ff4e8729b51fd5f3f69115001CVF · CVPR 2026

Ultra-Low Bitrate Perceptual Image Compression with Shallow Encoder

Zhang, Tianyu, Liu, Dong, Chen, Chang Wen

原文摘要

Ultra-low bitrate image compression (below 0.05 bits per pixel) is increasingly critical for bandwidth-constrained and computation-limited encoding scenarios such as edge devices. Existing frameworks typically rely on large pretrained encoders (e.g., VAEs or tokenizer-based models) and perform transform coding within their generative latent space. While these approaches achieve impressive perceptual fidelity, their reliance on heavy encoder networks makes them unsuitable for deployment on weak sender devices. In this work, we explore the feasibility of applying shallow encoders for ultra-low bitrate compression and propose a novel Asymmetric Extreme Image Compression (AEIC) framework that pursues simultaneously encoding simplicity and decoding quality. Specifically, AEIC employs moderate or even shallow encoder networks, while leveraging an one-step diffusion decoder to maintain high-fidelity and high-realism reconstructions under extreme bitrates. To further enhance the efficiency of shallow encoders, we design a dual-side feature distillation scheme that transfers knowledge from AEIC with moderate encoders to its shallow encoder variants. Experiments show that AEIC not only outperforms existing methods on rate-distortion-perception performance at ultra-low bitrates, but also delivers exceptional encoding efficiency for 35.8 FPS on 1080P images, while maintaining competitive decoding speed compared to existing methods. Code is available at https://github.com/LuizScarlet/AEIC.

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:252e6fe036f806eb91f283bc8dd669b9CVF · CVPR 2026

ViBES: A Conversational Agent with Behaviorally-Intelligent 3D Virtual Body

Zhang, Juze, Chen, Changan, Chen, Xin, Yu, Heng, Xiang, Tiange, Khan, Ali Sartaz, Lakshmikanth, Shrinidhi K., Adeli, Ehsan

原文摘要

Human communication is inherently multimodal and social: words, prosody, and body language jointly carry intent. Yet most prior systems model human behavior as a translation task--co-speech gesture or text-to-motion that maps a fixed utterance to motion clips--without requiring agentic decision-making about when to move, what to do, or how to adapt across multi-turn dialogue. This leads to brittle timing, weak social grounding, and fragmented stacks where speech, text, and motion are trained or inferred in isolation. We introduce ViBES (Voice in Behavioral Expression and Synchrony), a conversational 3D agent that jointly plans language and movement and executes dialogue-conditioned body actions. Concretely, ViBES is a speech-language-behavior (SLB) model with a mixture-of-modality-experts (MoME) backbone: modality-partitioned transformer experts for speech, facial expression, and body motion. The model processes interleaved multimodal token streams with hard routing by modality (parameters are split per expert), while sharing information through cross-expert attention. By leveraging strong pretrained speech-language models, the agent supports mixed-initiative interaction: users can speak, type, or issue body-action directives mid-conversation, and the system exposes controllable behavior hooks for streaming responses. We further benchmark on multi-turn conversation with automatic metrics of dialogue-motion alignment and behavior quality, and observe consistent gains over strong co-speech and text-to-motion baselines. ViBES goes beyond "speech-conditioned motion generation" toward agentic virtual bodies where language, prosody, and movement are jointly generated, enabling controllable, socially competent 3D interaction.

cs.CV · cs.CL · cs.AI

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:93253ca96f6422578d392e8dbd790817CVF · CVPR 2026

Joint Spectral Image Reconstruction and Semantic Segmentation with Cooperative Unfolding

He, Zijun, Wang, Ping, Wang, Xiaodong, Chen, Chang, Yuan, Xin

原文摘要

Coded Aperture Snapshot Spectral Imaging (CASSI) is an emerging hyperspectral image (HSI) acquisition technique for downstream semantic segmentation. Due to the ill-posedness nature of CASSI systems, typical solutions are compelled to conduct a two-stage reconstruction-then-segmentation pipeline, namely viewing them as two separate tasks. However, we observe that such two tasks are interrelated and mutually reinforcing for representation learning, and thus separating them limits the overall accuracy and efficiency. To this end, we propose the first Cooperative Reconstruction-Segmentation Deep Unfolding Network (CRSDUN) to solve the reconstruction and segmentation tasks in parallel. To make the two mutually reinforcing, we introduce the Cross-Aggregated Super-Token Attention (CASTA) mechanism to enhance the representation interactions between HSI reconstruction and semantic segmentation. Extensive experiments on both synthetic and real-world HSI reconstruction-segmentation datasets demonstrate that our method achieves state-of-the-art in both spectral reconstruction and semantic segmentation. The code is available at \href https://github.com/zjhe02/CRSDUN https://github.com/zjhe02/CRSDUN .

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:a499455afa217859e1f28c82cb5e6f21CVF · CVPR 2026

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

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:ab4f1d9f33c5065bc8a4601d67c2e365CVF · CVPR 2026

FastGaMer: Efficient GainMap Learning for Practical Inverse Tone Mapping

Guan, Yuanshen, Xu, Ruikang, Chen, Chang, Liao, Yinuo, Song, Dehua, Song, Fenglong, Xiong, Zhiwei

原文摘要

Inverse tone mapping (ITM) becomes significantly harder when the SDR input is produced by local tone mapping, which jointly applies global radiometric compression and spatially varying adaptations that distort dynamic range, contrast, and channel-wise color ratios. Existing ITM methods ignore this degradation structure and either regress HDR values directly or rely on a single-channel gain map, which scale luminance only and cannot restore the compressed dynamic range and wide color gamut.We introduce FastGaMer, a structured and resolution-agnostic ITM framework that explicitly mirrors this degradation process. Instead of regressing HDR values, we reconstruct a color gain map, which preserves per-channel amplification, simplifies learning, and enables proper gamut extension. Local and global degradations are inverted separately using dynamic bilateral grids and learnable 3D LUTs, followed by a lightweight neural modulator for global refinement and coherence. All high-resolution operations are network-free, yielding exceptional efficiency.To support color-GM supervision under realistic local TMO degradations, we create a dataset of over 8,000 4K SDR-GM pairs with an additional real-captured test set. FastGaMer outperforms prior lightweight ITM methods by +1.4 dB PQ-PSNR, reduces runtime by 70%, and processes 4K images in only 6.2 ms, achieving both high accuracy and real-time performance.

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:ac5173c49a356a6f8da5676e6550b4beCVF · CVPR 2026

BAgger: Backwards Aggregation for Mitigating Drift in Autoregressive Video Diffusion Models

Po, Ryan, Chan, Eric Ryan, Chen, Changan, Wetzstein, Gordon

原文摘要

Autoregressive video models are promising for world modeling via next-frame prediction, but they suffer from exposure bias: a mismatch between training on clean contexts and inference on self-generated frames, causing errors to compound and quality to drift over time. We introduce Backwards Aggregation (BAgger), a self-supervised scheme that constructs corrective trajectories from the model's own rollouts, teaching it to recover from its mistakes. Unlike prior approaches that rely on few-step distillation and distribution-matching losses --which can hurt quality and diversity -- BAgger trains with standard score or flow matching objectives, avoiding large teachers and long-chain backpropagation through time. We instantiate BAgger on causal diffusion transformers and evaluate on text-to-video, video extension, and multi-prompt generation, observing more stable long-horizon motion and better visual consistency with reduced drift.

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:d20c3e853c09a209de6ef974ac0bf87aCVF · CVPR 2026

Uncertainty-Aware Knowledge Distillation for Multimodal Large Language Models

Sun, Jingchen, Han, Shaobo, Patel, Deep, Kohno, Wataru, Jin, Can, Chen, Changyou

原文摘要

Knowledge distillation establishes a learning paradigm that leverages both data supervision and teacher guidance. However, determining the optimal balance between learning from data and learning from the teacher is challenging, as some samples may be noisy while others are subject to teacher uncertainty. This motivates the need for adaptively balancing data and teacher supervision. We propose Beta-weighted Knowledge Distillation (Beta-KD), an uncertainty-aware distillation framework that adaptively modulates how much the student relies on teacher guidance. Specifically, we formulate teacher-student learning from a unified Bayesian perspective and interpret teacher supervision as a Gibbs prior over student activations. This yields a closed-form, uncertainty-aware weighting mechanism and supports arbitrary distillation objectives and their combinations. Extensive experiments are conducted on multimodal VQA benchmarks by distilling a student Vision-Language Model from a large teacher VLM. The results demonstrate that Beta-KD consistently outperforms existing knowledge distillation methods. Code is available at https://github.com/Jingchensun/beta-kd.

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:d3b5d81d18e2bbccd8df7660b51caa51CVF · CVPR 2026

RawMetaDiff: Unlocking Extreme Darkness from Dual-Exposure RAW with Meta-Guided Diffusion

Liu, Panjun, Xia, Jiyuan, Guan, Yuanshen, Li, Yong, Lang, Zhiqiang, Xu, Ruikang, Chen, Chang, Song, Dehua 等 10 位作者

原文摘要

Extreme low-light Raw image restoration remains challenging due to overwhelming noise and severe detail loss.In this paper, we exploit the potential of the dual-exposure setting for this severely ill-posed problem.Existing methods suffer from unreliable cross-exposure alignment, resulting in degraded detail recovery and compromised color fidelity. To address these challenges, we propose RawMetaDiff, a novel generative diffusion framework that restores a high-fidelity Raw image from a short-exposure input, conditioned on a potentially misaligned long-exposure reference under the guidance of Raw metadata.At its core, we propsed two complementary mechanisms: the Meta-Assistant Color Transfer (MACT) enforces color consistency by aligning global color statistics along the channel dimension,while the Meta-Normed Cross Attention (MNCA) leverages Raw metadata to establish robust cross-exposure spatial correspondences and inject shadow details.To support robust diffusion training, we first collect a 1K real-world, dual-exposure Raw dataset, namely DERaw, and then design a realistic degradation model to synthesize data that closely approximates real-world conditions.Extensive experiments on both synthetic and real-world datasets demonstrate that RawMetaDiff significantly outperforms existing methods, justifying an effective new solution for extreme low-light Raw image restoration from the generative perspective.

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:e86ac5e68cf3ef1d8f9086b77219c2beCVF · CVPR 2026

Generalizing Visual Geometry Priors to Sparse Gaussian Occupancy Prediction

Zhou, Changqing, Luo, Yueru, Chen, Changhao

原文摘要

Accurate 3D scene understanding is essential for embodied intelligence, with occupancy prediction emerging as a key task for reasoning about both objects and free space. Existing approaches largely rely on depth priors (e.g., DepthAnything) but make only limited use of 3D cues, restricting performance and generalization. Recently, visual geometry models such as VGGT have shown strong capability in providing rich 3D priors, but similar to monocular depth foundation models, they still operate at the level of visible surfaces rather than volumetric interiors, motivating us to explore how to more effectively leverage these increasingly powerful geometry priors for 3D occupancy prediction. We present GPOcc, a framework that leverages generalizable visual geometry priors (GPs) for monocular occupancy prediction. Our method extends surface points inward along camera rays to generate volumetric samples, which are represented as Gaussian primitives for probabilistic occupancy inference. To handle streaming input, we further design a training-free incremental update strategy that fuses per-frame Gaussians into a unified global representation. Experiments on Occ-ScanNet and EmbodiedOcc-ScanNet demonstrate significant gains: GPOcc improves mIoU by +9.99 in the monocular setting and +11.79 in the streaming setting over prior state of the art. Under the same depth prior, it achieves +6.73 mIoU while running 2.65 times faster. These results highlight that GPOcc leverages geometry priors more effectively and efficiently. Code will be released at https://github.com/JuIvyy/GPOcc.

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 ↗