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首次观测 · 记录 #1CVF · CVPR 2026:cvf:346fb91288952ad65015d69be0a4d86fCVF · CVPR 2026

ALLNet: Multi-task Dense Prediction for Degraded Images

Wang, Weiran, Wu, Jialing, Chang, Yaqi, He, Gang, Xu, Li, Wu, Chang, Li, Yunsong

原文摘要

Multi-taskdensepredictionaims tosimultaneously addressmultiplepixel-level tasksthroughaunifiednetworkforvisualsceneunderstanding.However,adverse environmental conditions limit thegeneralizationand practicality of such tasks. Toaddress this, we proposeALLNet, anovel framework that effectively explores degradationpatterns and integratesmulti-task collaborative information. Specifically, we designa MoE-basedMixtureofAdaptiveExperts(MaE)restorationcomponentnetworkthatenhancesdegradationfeaturesthroughdynamicroutingandguidestask-specific featureextraction. Furthermore,weformulateaTaskawareCollaborativeRefinement (TCR)moduletocaptureglobalsemanticcorrelationsandcross-taskdependencies,facilitatingbidirectionalcollaborationbetween restorationandtask-specific featuresondegradedimages. Tothebestofourknowledge, thisrepresentsthe firstattemptatmulti-taskdensepredictionunderimage degradation.ExperimentalresultsondegradedNYUDv2andPASCAL-Contextbenchmarksdemonstratethat ourarchitecturesignificantlyoutperformsexistingmethodsindegradedscenarios.

cs.CV

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

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:43e6cc0dd0fbacb0bc68ae7c67f72587CVF · CVPR 2026

FastHybrid: Accelerating Hybrid Autoregressive Image Generation with Lookahead and Guided Decoding

Jiang, Zhengguo, Zhang, Fang, Hua, Yongxiang, Li, Bocheng, Zhang, Wentao, Xu, Linli

原文摘要

Autoregressive (AR) models have achieved remarkable success in natural language processing, yet their application to image generation faces significant challenges. When implementing VQ-based decoders for autoregressive image generation, the generated images typically preserve semantic information but struggle with fine-grained details. Recent hybrid AR image generation approaches address these issues by integrating diffusion models as decoder heads, enabling more high-fidelity generation. However, the diffusion-based denoising process introduces significant computational overhead during inference.To accelerate hybrid AR image generation, we propose the Lookahead Decoding Strategy, which integrates the strengths of autoregressive and diffusion models by separating the process into two complementary branches: semantic prediction and detail refinement. The autoregressive branch captures high-level semantic structures while refining coarse predictions made by the parallel. Furthermore, we introduce Guided Diffusion Sampling to steer the diffusion denoising trajectory, significantly reducing the number of denoising steps. Extensive experiments demonstrate that our approach provides an effective solution for accelerating hybrid AR image generation models.

cs.CV

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

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:54412e5f4787d2ccd4da7385fe672677CVF · CVPR 2026

DiG: Differential Grounding for Enhancing Fine-Grained Perception in Multimodal Large Language Models

Tao, Zhou, Wang, Shida, Hua, YongXiang, Cao, Haoyu, Xu, Linli

原文摘要

Multimodal Large Language Models have achieved impressive performance on a variety of vision-language tasks, yet their fine-grained visual perception and precise spatial reasoning remain limited. In this work, we introduce DiG (Differential Grounding), a novel proxy task framework where MLLMs learn fine-grained perception by identifying and localizing all differences between similar image pairs without prior knowledge of their number. To support scalable training, we develop an automated 3D rendering-based data generation pipeline that produces high-quality paired images with fully controllable discrepancies. To address the sparsity of difference signals, we further employ curriculum learning that progressively increases complexity from single to multiple differences, enabling stable optimization. Extensive experiments demonstrate that DiG significantly improves model performance across a variety of visual perception benchmarks and that the learned fine-grained perception skills transfer effectively to standard downstream tasks, including RefCOCO, RefCOCO+, RefCOCOg, and general multimodal perception benchmarks. Our results highlight differential grounding as a scalable and robust approach for advancing fine-grained visual reasoning in MLLMs.

cs.CV · cs.CL

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

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:5b58590a102f029ff3a453151280dd28CVF · CVPR 2026

TableMix: Enhancing Multimodal Table Reasoning in MLLMs from a Data-Centric Perspective

Liu, Chaohu, Wang, Shida, Wang, Yubo, Xu, Linli

原文摘要

Recent advances in Multimodal Large Language Models (MLLMs) have enabled promising progress in table reasoning from visual table inputs. Despite their ability to capture rich visual cues such as color and layout, MLLMs still underperform compared to text-only models.We argue that a major limitation lies in the pre-training process, which inadvertently weakens the model's intrinsic reasoning ability and consequently hinders the effectiveness of reinforcement fine-tuning on table reasoning tasks.In this paper, we introduce TableMix, a novel framework that tackles this challenge from a data-centric perspective. At the core of TableMix is a principled data mixing strategy. Specifically, TableMix constructs a hybrid dataset that combines: (1) multimodal table reasoning data to improve task-specific reasoning, (2) text-only mathematical reasoning data to revive the model's logical competence, and (3) simple multimodal perception data to preserve visual grounding.Recognizing the non-uniform difficulty of mixed data, we further propose a Difficulty-Aware Reward Shaping (DRS) mechanism, which enables the Group Relative Policy Optimization (GRPO) algorithm to adaptively reward concise reasoning for easy problems while encouraging more elaborate reasoning for complex ones, thereby reducing redundant computation and errors.Extensive experiments show that TableMix markedly enhances the reasoning ability of MLLMs, outperforming strong multimodal baselines and even rivaling state-of-the-art text-only models.

cs.CV · cs.CL

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

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:acabe626c7a373283a163f2a04d7f887CVF · CVPR 2026

Hilbert Curve-Based Attention Enabling Topology-Preserving Image Tensor Representation for Semantic Segmentation Network

Xu, Linkang, Li, Gang, Song, Yue, Ji, Xiangxin

原文摘要

Drone-based building defect segmentation remains challenging due to complex surface textures and illumination variations. We propose TPSegformer, a topology-preserving segmentation framework that mitigates mis-segmentation in such scenarios. Its decoder incorporates a Hilbert curve-based topology-preserving mechanism to maintain spatial continuity and boundary precision during category layer computation. A lightweight multi-scale fusion module enhances semantic representation, while global context modeling strengthens holistic perception. Experiments on the building defect dataset show that TPSegformer outperforms existing segmentation methods, achieving 80.77% mIoU and 90.22% Acc. On the Dacl10k dataset, it maintains strong generalization, reaching 44.27% mIoU and 60.32% Acc across diverse materials and defect types. The code is available at : https://github.com/mumu-k/TPSegformer

cs.CV

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

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:dbc3f9e2ab9f77cb5a1e61578cfb39c2CVF · CVPR 2026

VisRef: Visual Refocusing while Thinking Improves Test-Time Scaling in Multi-Modal Large Reasoning Models

Ghosal, Soumya Suvra, Kim, Youngeun, Li, Zhuowei, Chaudhry, Ritwick, Xu, Linghan, Zhang, Hongjing, Zablocki, Jakub, Xing, Yifan 等 9 位作者

原文摘要

Advances in large reasoning models have shown strong performance on complex reasoning tasks by scaling test-time compute through extended inference-time thinking. However, recent studies observe that in vision-dependent tasks, extended textual reasoning at inference time can often degrade performance as models progressively lose attention to visual tokens, increasingly relying on textual priors alone. To address this, prior works used reinforcement learning (RL)-based fine-tuning to route visual tokens or employ refocusing mechanisms during reasoning. While effective, these methods are computationally expensive, requiring large-scale data generation and policy optimization. To leverage the benefits of inference-time compute without additional RL fine-tuning, we propose VisRef, a visually grounded test-time scaling framework. Our key idea is to actively guide the reasoning process through re-injecting a coreset of visual tokens that are semantically relevant to the reasoning context yet diverse and globally representative of the image for more grounded multi-modal reasoning. Experiments on three visual reasoning benchmarks with state-of-the-art multi-modal large reasoning models demonstrate that under fixed inference-time compute budgets, VisRef consistently outperforms existing test-time scaling approaches by up to 6.4%.

cs.CV

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

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:edb444ec78e5c8263ea4f9847ad20ed3CVF · CVPR 2026

Asking like Socrates: Socrates helps VLMs understand remote sensing images

Shao, Run, Li, Ziyu, Zhang, Zhaoyang, Xu, Linrui, He, Xinran, Yuan, Hongyuan, He, Bolei, Dai, Yongxing 等 12 位作者

原文摘要

Recent multimodal reasoning models, inspired by DeepSeek-R1, have significantly advanced vision-language systems. However, in remote sensing (RS) tasks, we observe widespread pseudo reasoning: models narrate the process of reasoning rather than genuinely reason toward the correct answer based on visual evidence. We attribute this to the Glance Effect, where a single, coarse perception of large-scale RS imagery results in incomplete understanding and reasoning based on linguistic self-consistency instead of visual evidence. To address this, we propose RS-EoT (Remote Sensing Evidence-of-Thought), a language-driven, iterative visual evidence-seeking paradigm. To instill this paradigm, we propose SocraticAgent, a self-play multi-agent system that synthesizes reasoning traces via alternating cycles of reasoning and visual inspection. To enhance and generalize these patterns, we propose a two-stage progressive RL strategy: first, RL on fine-grained Grounding tasks to enhance RS-EoT capabilities, followed by RL on RS VQA to generalize to broader understanding scenarios. Experiments show RS-EoT achieves state-of-the-art performance on multiple RS VQA and grounding benchmarks. Analyses reveal clear iterative cycles of reasoning and evidence seeking, confirming RS-EoT mitigates the Glance Effect and enables genuine evidence-grounded reasoning. Our code, data, and models are available at https://geox-lab.github.io/Asking_like_Socrates/.

cs.CV · cs.MA

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

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:fc20e0981dd18c7a27efd4320d4f048aCVF · CVPR 2026

Towards Unified Human Perception and Machine Understanding: Token Flow Guided Compression Framework

Xu, Li, Zhang, Yingfu, Xu, Kepeng, He, Gang, Li, Yunsong

原文摘要

With the rapid rise of Large Vision Language Models (LVLMs) for image understanding, the objective of image compression is gradually shifting from human visual perception to machine-oriented semantic understanding. However, conventional learned compression techniques are optimized for pixel-level fidelity and typically operate at fixed or rigid bitrate points, misaligned with the semantic consistency and flexible bitrate control. This gap becomes critical in ultra-low-bitrate regimes, where latent representations often ignore semantic relevance and struggle to disentangle meaningful content from redundant visual details as the bitrate varies. To address these challenges, we develop a token-based flexible compression framework, Token Flow Guided Compression (TFGC), which unifies human- and machine-oriented objectives. TFGC supports variable bitrate control in ultra-low bitrate regimes and enables LVLMs to directly process compressed token without image reconstruction. Specifically, we explore token flow phenomenon in 1D token sequences and exploit it to design token flow propagation, which predicts missing tokens by propagating contextual information from unmasked tokens. Moreover, token semantic guidance aligns compressed representations with LVLM semantic space, while a progressive semantic alignment training strategy further bridges the gap between perceptual reconstruction and semantic reasoning. Experiments show that our framework achieves state-of-the-art LVLMs understanding at comparable bitrates while maintaining satisfactory perceptual quality.

cs.CV · cs.CL

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

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:fea5f33e5fe0df7a7558eaf7c7b3033cCVF · CVPR 2026

SAVA-X: Ego-to-Exo Imitation Error Detection via Scene-Adaptive View Alignment and Bidirectional Cross View Fusion

Li, Xiang, Qiu, Heqian, Wang, Lanxiao, Qiu, Benliu, Meng, Fanman, Xu, Linfeng, Li, Hongliang

原文摘要

Error detection is crucial in industrial training, healthcare, and assembly quality control. Most existing work assumes a single-view setting and cannot handle the practical case where a third-person (exo) demonstration is used to assess a first-person (ego) imitation. We formalize Ego->Exo Imitation Error Detection: given asynchronous, length-mismatched ego and exo videos, the model must localize procedural steps on the ego timeline and decide whether each is erroneous. This setting introduces cross-view domain shift, temporal misalignment, and heavy redundancy. Under a unified protocol, we adapt strong baselines from dense video captioning and temporal action detection and show that they struggle in this cross-view regime. We then propose SAVA-X, an Align-Fuse-Detect framework with (i) view-conditioned adaptive sampling, (ii) scene-adaptive view embeddings, and (iii) bidirectional cross-attention fusion. On the EgoMe benchmark, SAVA-X consistently improves AUPRC and mean tIoU over all baselines, and ablations confirm the complementary benefits of its components.

cs.CV

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

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CVF · CVPR 2026 ↗PDF ↗