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首次观测 · 记录 #1CVF · CVPR 2026:cvf:96e44a5b510673917b3e05024f1927b9CVF · CVPR 2026

I2I-Bench: A Comprehensive Benchmark Suite for Image-to-Image Editing Models

Wang, Juntong, Wang, Jiarui, Duan, Huiyu, Kang, Jiaxiang, Zhai, Guangtao, Min, Xiongkuo

原文摘要

Image editing models are advancing rapidly, yet comprehensive evaluation remains a significant challenge. Existing image editing benchmarks generally suffer from limited task scopes, insufficient evaluation dimensions, and heavy reliance on manual annotations, which significantly constrain their scalability and practical applicability. To address this, we propose I2I-Bench, a comprehensive benchmark for image-to-image editing models, which features (i) diverse tasks, encompassing 10 task categories across both single-image and multi-image editing tasks, (ii) comprehensive evaluation dimensions, including 30 decoupled and fine-grained evaluation dimensions with automated hybrid evaluation methods that integrate specialized tools and large multimodal models (LMMs), and (iii) rigorous alignment validation, justifying the consistency between our benchmark evaluations and human preferences.Using I2I-Bench, we benchmark numerous mainstream image editing models, investigating the gaps and trade-offs between editing models across various dimensions. We will open-source all components of I2I-Bench to facilitate future research.

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:970ca4d541c94075ee418b48f96bf9b1CVF · CVPR 2026

Will Multimodal Models Be Dazzled by Multi-Image Visual Puzzles?

Zhu, Zhi, Fan, YaoQi, Chen, Zhe, Cao, Yue, Liu, Yangzhou, Lu, Tong

原文摘要

The rapid advancement of Multimodal Large Language Models (MLLMs) has revealed the limitations of existing benchmarks in evaluating complex reasoning over multiple images. To address this gap, we introduce MIRACLE, a novel benchmark for Multi-Image complex Reasoning And Comprehension Logic Evaluation, featuring 4,000 questions across diverse reasoning types such as visual comparison, temporal sequencing, and spatial relations. MIRACLE emphasizes strong inter-image dependencies through a systematic data collection process, followed by delicate instance grouping and question design that enforce cross-image reasoning.Evaluation on leading MLLMs shows that even top-performing models like Gemini-2.5-Pro achieve only 55.91% points, highlighting the significant challenges of multi-image reasoning. Moreover, in scenarios characterized by high visual information density, such as puzzle tasks and ultra multi-image input conditions, all models exhibit a significant drop in performance, which highlights the limitations of MLLMs in handling complex structural relations and collaborative reasoning, revealing deficiencies in their cognitive capabilities under high-load visual reasoning settings. We hope MIRACLE will inspire the community to push the boundaries of multi-image reasoning. MIRACLE is available at https://huggingface.co/datasets/queyuecanyang/MIRACLE.

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:972fd342bfad69e873afd38a075702e5CVF · CVPR 2026

QuadSync: Quadrifocal Tensor Synchronization via Tucker Decomposition

Miao, Daniel, Lerman, Gilad, Kileel, Joe

原文摘要

In structure from motion, quadrifocal tensors capture more information than their pairwise counterparts (essential matrices), yet they have often been thought of as impractical and only of theoretical interest. In this work, we challenge such beliefs by providing a new framework to recover n cameras from the corresponding collection of quadrifocal tensors. We form the block quadrifocal tensor and show that it admits a Tucker decomposition whose factor matrices are the stacked camera matrices, and which thus has a multilinear rank of (4,4,4,4) independent of n. We develop the first synchronization algorithm for quadrifocal tensors, using Tucker decomposition, alternating direction method of multipliers, and iteratively reweighted least squares. We further establish relationships between the block quadrifocal, trifocal, and bifocal tensors, and introduce an algorithm that jointly synchronizes these three entities. Numerical experiments demonstrate the effectiveness of our methods on modern datasets, indicating the potential and importance of using higher-order information in synchronization.

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:973167f718459737a3b02e3f775a25afCVF · CVPR 2026

Not All Birds Look The Same: Identity-Preserving Generation For Birds

Sun, Aaron, Saha, Oindrila, Maji, Subhransu

原文摘要

Since the advent of controllable image generation, increasingly rich modes of control have enabled greater customization and accessibility for everyday users.Zero-shot, identity-preserving models such as Insert Anything and OminiControl now support applications like virtual try-on without requiring additional fine-tuning.While these models may be fitting for humans and rigid everyday objects, they still have limitations for non-rigid or fine-grained categories. These domains often lack accessible, high-quality data--especially videos or multi-view observations of the same subject--making them difficult both to evaluate and to improve upon. Yet, such domains are essential for moving beyond content creation toward applications that demand accuracy and fine detail.Birds are an excellent domain for this task: they exhibit high diversity, require fine-grained cues for identification, and come in a wide variety of poses. We introduce the NABirds Look-Alikes (NABLA) dataset, consisting of 4,759 expert-curated image pairs. Together with 1,073 pairs collected from multi-image observations on iNaturalist and a small set of videos, this forms a benchmark for evaluating identity-preserving generation of birds.We show that state-of-the-art baselines fail to maintain identity on this dataset, and we demonstrate that training on images grouped by species, age, and sex--used as a proxy for identity--substantially improves performance on both seen and unseen species.

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:9731cdf04dd5c1ae9159edf7afcce434CVF · CVPR 2026

OraPO: Oracle-educated Reinforcement Learning for Data-efficient and Factual Radiology Report Generation

Chen, Zhuoxiao, Yu, Hongyang, Xu, Ying, Luo, Yadan, Duong, Long, Li, Yuan-Fang

原文摘要

Radiology report generation (RRG) aims to automatically produce clinically faithful reports from chest X-ray images. Prevailing work typically follows a scale-driven paradigm, by multi-stage training over large paired corpora and oversized backbones, making pipelines highly data- and compute-intensive. In this paper, we propose Oracle-educated GRPO (OraPO) with a FactScore-based reward (FactS) to tackle the RRG task under constrained budgets. OraPO enables single-stage, RL-only training by converting failed GRPO explorations on rare or difficult studies into direct preference supervision via a lightweight oracle step. FactS grounds learning in diagnostic evidence by extracting atomic clinical facts and checking entailment against ground-truth labels, yielding dense, interpretable sentence-level rewards. Together, OraPO and FactS create a compact and powerful framework that significantly improves learning efficiency on clinically challenging cases, setting the new SOTA performance on the CheXpert Plus dataset (0.341 in F1) with 2-3 orders of magnitude less training data using a small base VLM on modest hardware.

cs.CV

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:97534160ef7f1addd81161587879193eCVF · CVPR 2026

Learning Compact 3D Representations from Feed-Forward Novel View Synthesis

An, Honggyu, Jung, Jaewoo, Kim, Mungyeom, Kim, Chaehyun, Jeon, Minkyeong, Han, Jisang, Fukuda, Kazumi, Narihira, Takuya 等 13 位作者

原文摘要

Reconstructing and understanding 3D scenes from unposed sparse views in a feed-forward manner remains as a challenging task in 3D computer vision. Recent approaches use per-pixel 3D Gaussian Splatting for reconstruction, followed by a 2D-to-3D feature lifting stage for scene understanding. However, they generate excessive redundant Gaussians, causing high memory overhead and sub-optimal multi-view feature aggregation, leading to degraded novel view synthesis and scene understanding performance. We propose C3G, a novel feed-forward framework that estimates compact 3D Gaussians only at essential spatial locations, minimizing redundancy while enabling effective feature lifting. We introduce learnable tokens that aggregate multi-view features through self-attention to guide Gaussian generation, ensuring each Gaussian integrates relevant visual features across views. We then exploit the learned attention patterns for Gaussian decoding to efficiently lift features. Extensive experiments on pose-free novel view synthesis, 3D open-vocabulary segmentation, and view-invariant feature aggregation demonstrate our approach's effectiveness. Results show that a compact yet geometrically meaningful representation is sufficient for high-quality scene reconstruction and understanding, achieving superior memory efficiency and feature fidelity compared to existing methods.

cs.CV

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:9754817159b4eca667611b0dbbf532faCVF · CVPR 2026

InsCal: Calibrated Multi-Source Fully Test-Time Prompt Tuning for Object Detection

Que, Xiaofan, Wang, Dingrong, Liu, Xumin, Yu, Qi

原文摘要

Test-time prompt tuning (TPT) has emerged as a powerful technique for adapting pre-trained vision-language models (VLMs) to diverse downstream tasks, including image classification and visual reasoning. With the rise of text-driven object detectors, we extend TPT to object detection, unlocking new capabilities for cross-domain adaptation. However, a critical challenge in TPT is the inherent miscalibration caused by entropy minimization: domain shifts often lead to incorrect predictions, and enforcing high confidence exacerbates miscalibration, ultimately degrading performance. To tackle this, we introduce InsCal, a novel framework designed to enhance cross-domain object detection through three key innovations: (1) extending TPT to a multi-source paradigm, enabling knowledge aggregation across diverse domains; (2) reducing domain gaps via a novel text-driven style transfer strategy that aligns features to the source domain without requiring reference images; and (3) refining the entropy minimization objective with instance-specific calibration, ensuring robust and well-calibrated adaptation. Our approach not only mitigates miscalibration but also significantly improves cross-domain object detection performance, setting a new benchmark for test-time adaptation in VLMs.

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:976cb67d99571475496dd91e76c04280CVF · CVPR 2026

Decoupling Defense Strategies for Robust Image Watermarking

Chen, Jiahui, Deng, Zehang, Zhang, Zeyu, Li, Chaoyang, Jia, Lianchen, Sun, Lifeng

原文摘要

Deep learning-based image watermarking, while robust against conventional distortions, remains vulnerable to advanced adversarial and regeneration attacks. Conventional countermeasures, which jointly optimize the encoder and decoder via a noise layer, face 2 inevitable challenges:(1) decrease of clean accuracy due to decoder adversarial training and (2) limited robustness due to simultaneous training of all three advanced attacks.To overcome these issues, we propose AdvMark, a novel two-stage fine-tuning framework that decouples the defense strategies. In stage 1, we address adversarial vulnerability via a tailored adversarial training paradigm that primarily fine-tunes the encoder while only conditionally updating the decoder. This approach learns to move the image into a non-attackable region, rather than modifying the decision boundary, thus preserving clean accuracy.In stage 2, we tackle distortion and regeneration attacks via direct image optimization. To preserve the adversarial robustness gained in stage 1, we formulate a principled, constrained image loss with theoretical guarantees, which balances the deviation from cover and previous encoded images. We also propose a quality-aware early-stop to further guarantee the lower bound of visual quality.Extensive experiments demonstrate AdvMark outperforms with the highest image quality and comprehensive robustness, i.e. up to 29%, 33% and 46% accuracy improvement for distortion, regeneration and adversarial attacks, respectively.

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:977a11b3035e84900fe21af03fb64c10CVF · CVPR 2026

Beyond Semantic Search: Towards Referential Anchoring in Composed Image Retrieval

Yang, Yuxin, Zhou, Yinan, Chen, Yuxin, Zhang, Ziqi, Ma, Zongyang, Yuan, Chunfeng, Li, Bing, Gao, Jun 等 9 位作者

原文摘要

Composed Image Retrieval (CIR) has demonstrated significant potential by enabling flexible, multimodal queries that combine a reference image and modification text. However, CIR inherently prioritizes semantic matching, struggling to reliably retrieve a user-specified instance across contexts. In practice, emphasizing concrete instance fidelity over broad semantics is often more consequential. In this work, we propose Object-Anchored Composed Image Retrieval (OACIR), a novel fine-grained retrieval task that mandates strict instance-level consistency. To advance research on this task, we construct OACIRR (OACIR on Real-world images), the first large-scale, multi-domain benchmark comprising over 160K quadruples and four challenging candidate galleries enriched with hard-negative instance distractors. Each quadruple augments the compositional query with a bounding box that visually anchors the object in the reference image, providing a precise and flexible way to ensure instance preservation. To address the OACIR task, we propose AdaFocal, a framework featuring a Context-Aware Attention Modulator that adaptively intensifies attention within the specified instance region, dynamically balancing focus between the anchored instance and the broader compositional context. Extensive experiments demonstrate that AdaFocal substantially outperforms existing compositional retrieval models, particularly in maintaining instance-level fidelity, thereby establishing a robust baseline for this challenging task while opening new directions for more flexible, instance-aware retrieval systems.

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:977b64fd2fedf5bb3882115001536a3eCVF · CVPR 2026

SURF: Signature-Retained Fast Video Generation

Ding, Kaixin, Chen, Xi, Ji, Sihui, Gao, Yuan, Hou, Liang, Tao, Xin, Zhao, Hengshuang

原文摘要

The demand for high-resolution video generation is growing rapidly. However, the generation resolution is severely constrained by slow inference speeds. For instance, Wan2.1 requires over 50 minutes to generate a single 720p video. While previous works explore accelerating video generation from various aspects, most of them compromise the distinctive signatures (e.g., layout, semantic, motion) of the original model. In this work, we propose **SURF**, an efficient framework for generating high-resolution videos, while maximally keeping the signatures. Specifically, SURF divides video generation into two stages: First, we leverage the pretrained model to infer at optimal resolution and downsample latent to generate low-resolution previews in fast speed; then we design a Refiner to upscale the preview. In the preview stage, we identify that directly inferring a model (trained with higher resolution) on lower resolution causes severe losses in signatures. So we introduce noise reshifting, a training-free technique that mitigates this issue by conducting initial denoising steps on the original resolution and switching to low resolution in later steps. In the refine stage, we establish a mapping relationship between the preview and the high-resolution target, which significantly reduces the denoising steps. We further integrate shifting windows and carefully design the training paradigm to get a powerful and efficient Refiner. In this way, SURF enables generating high-resolution videos efficiently while maximally closer to the signatures of the given pretrained model. SURF is conceptually simple and could serve as a plug-in that is compatible with various base model and acceleration methods. For example, it achieves 12.5x speedup for generating 5-second, 16fps, 720p Wan 2.1 videos and 8.7x speedup for generating 5-second, 24fps, 720p HunyuanVideo.

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:97828ff65938a835edaa4b62a03c4c68CVF · CVPR 2026

Few-shot Acoustic Synthesis with Multimodal Flow Matching

Brunetto, Amandine

原文摘要

Generating audio that is acoustically consistent with a scene is essential for immersive virtual environments. Recent neural acoustic field methods enable spatially continuous sound rendering but remain scene-specific, requiring dense audio measurements and costly training for each environment. Few-shot approaches improve scalability across rooms but still rely on multiple recordings and, being deterministic, fail to capture the inherent uncertainty of scene acoustics under sparse context. We introduce FLow-matching ACoustic generation (FLAC), a probabilistic method for few-shot acoustic synthesis that models the distribution of plausible room impulse responses (RIRs) given minimal scene context. FLAC leverages a diffusion transformer trained with a flow-matching objective to generate RIRs at arbitrary positions in novel scenes, conditioned on spatial, geometric, and acoustic cues. FLAC outperforms state-of-the-art eight-shot baselines with one-shot on both the AcousticRooms and Hearing Anything Anywhere datasets. To complement standard perceptual metrics, we further introduce AGREE, a joint Acoustic-GeometRy EmbEdding, enabling geometry-consistent evaluation of generated RIRs through retrieval and distributional metrics. This work is the first to apply generative flow matching to explicit RIR synthesis, establishing a new direction for robust and data-efficient acoustic synthesis. Project page: https://amandinebtto.github.io/FLAC/

cs.CV

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:97881c500d598ba874e7a1246b97fcb0CVF · CVPR 2026

UniDex: A Robot Foundation Suite for Universal Dexterous Hand Control from Egocentric Human Videos

Zhang, Gu, Xu, Qicheng, Zhang, Haozhe, Ma, Jianhan, He, Long, Bao, Yiming, Ping, Zeyu, Yuan, Zhecheng 等 19 位作者

原文摘要

Dexterous manipulation remains challenging due to the cost of collecting real-robot teleoperation data, the heterogeneity of hand embodiments, and the high dimensionality of control. We present UniDex, a robot foundation suite that couples a large-scale robot-centric dataset with a unified vision-language-action (VLA) policy and a practical human-data capture setup for universal dexterous hand control. First, we construct UniDex-Dataset, a robot-centric dataset over 50K trajectories across eight dexterous hands (6-24 DoFs), derived from egocentric human video datasets. To transform human data into robot-executable trajectories, we employ a human-in-the-loop retargeting procedure to align fingertip trajectories while preserving plausible hand-object contacts, and we operate on explicit 3D pointclouds with human hands masked to narrow kinematic and visual gaps. Second, we introduce the Function-Actuator-Aligned Space (FAAS), a unified action space that maps functionally similar actuators to shared coordinates, enabling cross-hand transfer. Leveraging FAAS as the action parameterization, we train UniDex-VLA, a 3D VLA policy pretrained on UniDex-Dataset and finetuned with task demonstrations. In addition, we build UniDex-Cap, a simple portable capture setup that records synchronized RGB-D streams and human hand poses and converts them into robot-executable trajectories to enable human-robot data co-training that reduces reliance on costly robot demonstrations. On challenging tool-use tasks across two different hands, UniDex-VLA achieves 81% average task progress and outperforms prior VLA baselines by a large margin, while exhibiting strong spatial, object, and zero-shot cross-hand generalization. Together, UniDex-Dataset, UniDex-VLA, and UniDex-Cap provide a scalable foundation suite for universal dexterous manipulation.

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:9789392d4851f7331cfaa367a73a1bcfCVF · CVPR 2026

GOR-IS: 3D Gaussian Object Removal In the Intrinsic Space

Zhao, Yonghao, Gao, Yupeng, Yang, Jian, Xie, Jin, Wang, Beibei

原文摘要

Recent advances in Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have made it standard practice to reconstruct 3D scenes from multi-view images. Removing objects from such 3D representations is a fundamental editing task that requires complete and seamless inpainting of occluded regions, ensuring consistency in geometry and appearance. Although existing methods have made notable progress in improving inpainting consistency, they often neglect global lighting effects, leading to physically implausible results. Moreover, these methods struggle with view-dependent non-Lambertian surfaces, where appearance varies across viewpoints, leading to unreliable inpainting. In this paper, we present 3D Gaussian Object Removal in the Intrinsic Space (GOR-IS), a novel framework for physically consistent and visually coherent 3D object removal. Our approach decomposes the scene into intrinsic components and explicitly models light transport to maintain global lighting effects consistency. Furthermore, we introduce an intrinsic-space inpainting module that operates directly in the material and lighting domains, effectively addressing the challenges posed by non-Lambertian surfaces. Extensive experiments on both synthetic and real-world datasets demonstrate that our framework substantially improves the physical consistency and visual coherence of object removal, outperforming existing methods by 13% in perceptual similarity (LPIPS) and 2dB in peak signal-to-noise ratio (PSNR). Code is publicly available at https://applezyh.github.io/GOR-IS-project-page/

cs.CV

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:97a839cebb6485d9f76b4acada4c2839CVF · CVPR 2026

VGGT-360: Geometry-Consistent Zero-Shot Panoramic Depth Estimation

Yuan, Jiayi, Jiang, Haobo, Soh, De Wen, Zhao, Na

原文摘要

This paper presents VGGT-360, a novel training-free framework for zero-shot, geometry-consistent panoramic depth estimation. Unlike prior view-independent training-free approaches, VGGT-360 reformulates the task as panoramic reprojection over multi-view reconstructed 3D models by leveraging the intrinsic 3D consistency of VGGT-like foundation models, thereby unifying fragmented per-view reasoning into a coherent panoramic understanding. To achieve robust and accurate estimation, VGGT-360 integrates three plug-and-play modules that form a unified panorama-to-3D-to-depth framework: (i) Uncertainty-guided adaptive projection slices panoramas into perspective views to bridge the domain gap between panoramic inputs and VGGT's perspective prior. It estimates gradient-based uncertainty to allocate denser views to geometry-poor regions, yielding geometry-informative inputs for VGGT. (ii) Structure-saliency enhanced attention strengthens VGGT's robustness during 3D reconstruction by injecting structure-aware confidence into its attention layers, guiding focus toward geometrically reliable regions and enhancing cross-view coherence. (iii) Correlation-weighted 3D model correction refines the reconstructed 3D model by reweighting overlapping points using attention-inferred correlation scores, providing a consistent geometric basis for accurate panoramic reprojection. Extensive experiments show that VGGT-360 outperforms both trained and training-free state-of-the-art methods across multiple resolutions and diverse indoor and outdoor datasets. The code is available at https://github.com/Yuanjiayii/VGGT-360.

cs.CV

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:97c30a1f12e23dcf055e8865c6f03ac1CVF · CVPR 2026

WISER: Wider Search, Deeper Thinking, and Adaptive Fusion for Training-Free Zero-Shot Composed Image Retrieval

Wang, Tianyue, Qu, Leigang, Yang, Tianyu, Hao, Xiangzhao, Xu, Yifan, Guo, Haiyun, Wang, Jinqiao

原文摘要

Zero-Shot Composed Image Retrieval (ZS-CIR) aims to retrieve target images given a multimodal query (comprising a reference image and a modification text), without training on annotated triplets. Existing methods typically convert the multimodal query into a single modality--either as an edited caption for Text-to-Image retrieval (T2I) or as an edited image for Image-to-Image retrieval (I2I). However, T2I often loses fine-grained visual details, while I2I struggles with complex semantic modifications. To effectively leverage their complementary strengths under diverse query intents, we propose WISER, a training-free framework that unifies T2I and I2I via a "retrieve-verify-refine" pipeline, explicitly modeling intent awareness and uncertainty awareness. Specifically, WISER first performs Wider Search by generating both edited captions and images for parallel retrieval to broaden the candidate pool. Then, it conducts Adaptive Fusion with a verifier to assess retrieval confidence, triggering refinement for uncertain retrievals, and dynamically fusing the dual-path for reliable ones. For uncertain retrievals, WISER generates refinement suggestions through structured self-reflection to guide the next retrieval round toward Deeper Thinking. Extensive experiments show the superior performance of WISER across multiple benchmarks, achieving relative improvements of 45% on CIRCO (mAP@5) and 57% on CIRR (Recall@1) over existing training-free methods. Code is released at https://github.com/Physicsmile/WISER.

cs.CV

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:97e46b013048fb1447bdda6da02c7fa7CVF · CVPR 2026

Discriminative Perception via Anchored Description for Reasoning Segmentation

Yang, Tao, Zhou, Qing, Li, Yanliang, Wang, Qi

原文摘要

Reasoning segmentation increasingly employs reinforcement learning to generate explanatory reasoning chains that guide Multimodal Large Language Models. While these geometric rewards are primarily confined to guiding the final localization, they are incapable of discriminating whether the reasoning process remains anchored on the referred region or strays into irrelevant context. Lacking this discriminative guidance, the model's reasoning often devolves into unfocused and verbose chains that ultimately fail to disambiguate and perceive the target in complex scenes. This suggests a need to complement the RL objective with Discriminative Perception, an ability to actively distinguish a target from its context. To realize this, we propose DPAD to compel the model to generate a descriptive caption of the referred object, which is then used to explicitly discriminate by contrasting the caption's semantic relevance to the referred object against the wider context. By optimizing for this discriminative capability, the model is forced to focus on the unique attributes of the target, leading to a more converged and efficient reasoning chain. The descriptive caption also serves as an interpretability rationale that aligns with the segmentation. Experiments on the benchmarks confirm the validity of our approach, delivering substantial performance gains, with the cIoU on ReasonSeg increasing by 3.09% and the reasoning chain length decreasing by approximately 42%. Code is available at https://github.com/mrazhou/DPAD.

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:97f71a9abe9c85e05cbd92aecd394684CVF · CVPR 2026

Neural Gabor Splatting: Enhanced Gaussian Splatting with Neural Gabor for High-frequency Surface Reconstruction

Watanabe, Haato, Umetani, Nobuyuki

原文摘要

Recent years have witnessed the rapid emergence of 3D Gaussian Splatting (3DGS) as a powerful approach for 3D reconstruction and novel view synthesis. Its explicit representation with Gaussian primitives enables fast training, real-time rendering, and convenient post-processing such as editing and surface reconstruction. However, 3DGS suffers from a critical drawback: the number of primitives grows drastically for scenes with high-frequency appearance details, since each primitive can represent only a single color, requiring multiple primitives for every sharp color transition.To overcome this limitation, we propose Neural Gabor splatting, which augments each Gaussian primitive with a lightweight multi-layer perceptron (MLP) that models a wide range of color variations within a single primitive. To further control primitive numbers, we introduce a frequency-aware densification strategy that selects mismatch primitives for pruning and cloning based on frequency energy.Our method achieves accurate reconstruction of challenging high-frequency surfaces. We demonstrate its effectiveness through extensive experiments on both standard benchmarks, such as Mip-NerRF360 and high-frequency surface datasets (e.g., checkered patterns), supported by comprehensive ablation studies.

cs.CV

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

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Task-Driven Implicit Representations for Automated Design of LiDAR Systems

Behari, Nikhil, Young, Aaron, Klinghoffer, Tzofi, Dave, Akshat, Raskar, Ramesh

原文摘要

Imaging system design is a complex, time-consuming, and largely manual process; LiDAR design, ubiquitous in mobile devices, autonomous vehicles, and aerial imaging platforms, adds further complexity through unique spatial and temporal sampling requirements. In this work, we propose a framework for automated, task-driven LiDAR system design under arbitrary constraints. To achieve this, we represent LiDAR configurations in a continuous six-dimensional design space and learn task-specific implicit densities in this space via flow-based generative modeling. We then synthesize new LiDAR systems by modeling sensors as parametric distributions in 6D space and fitting these distributions to our learned implicit density using expectation-maximization, enabling efficient, constraint-aware LiDAR system design. We validate our method on diverse tasks in 3D vision, enabling automated LiDAR system design across real-world-inspired applications in face scanning, robotic tracking, and object detection.

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

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PARSE: Part-Aware Relational Spatial Modeling

Bai, Yinuo, Xu, Peijun, Shao, Kuixiang, Jiao, Yuyang, Zhang, Jingxuan, Yao, Kaixin, Gu, Jiayuan, Yu, Jingyi

原文摘要

Inter-object relations underpin spatial intelligence, yet existing representations--linguistic prepositions or object-level scene graphs--are too coarse to specify which regions actually support, contain, or contact one another, leading to ambiguous and physically inconsistent layouts. To address these ambiguities, a part-level formulation is needed; therefore, we introduce PARSE, a framework that explicitly models how object parts interact to determine feasible and spatially grounded scene configurations. PARSE centers on the Part-centric Assembly Graph (PAG), which encodes geometric relations between specific object parts, and a Part-Aware Spatial Configuration Solver that converts these relations into geometric constraints to assemble collision-free, physically valid scenes. Using PARSE, we build PARSE-10K, a dataset of 10,000 3D indoor scenes constructed from real-image layout priors and a curated part-annotated shape database, each with dense contact structures and a part-level contact graph. With this structured, spatially grounded supervision, fine-tuning Qwen3-VL on PARSE-10K yields stronger object-level layout reasoning and more accurate part-level relation understanding; furthermore, leveraging PAGs as structural priors in 3D generation models leads to scenes with substantially improved physical realism and structural complexity. Together, these results show that PARSE significantly advances geometry-grounded spatial reasoning and supports the generation of physically consistent 3D scenes.

cs.CV

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

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ActiveAD: Planning-Oriented Active Learning for End-to-End Autonomous Driving

Lu, Han, Jia, Xiaosong, Xie, Yichen, Sun, Siyu, Liao, Wenlong, Yang, Xiaokang, Yan, Junchi

原文摘要

End-to-end differentiable learning has emerged as a prominent paradigm in autonomous driving (AD). A significant bottleneck in this approach is its substantial demand for high-quality labeled data, such as 3D bounding boxes and semantic segmentation, which are especially expensive to annotate manually. This challenge is exacerbated by the long tailed distribution in AD datasets, where a substantial portion of the collected data might be trivial (e.g. simply driving straight on a straight road) and only a minority of instances are critical to safety. In this paper, we propose ActiveAD, a planning-oriented active learning strategy designed to enhance sampling and labeling efficiency in end-to-end autonomous driving. ActiveAD progressively annotates parts of collected raw data based on our newly developed metrics. We design innovative diversity metrics to enhance initial sample selection, addressing the cold-start problem. Furthermore, we develop uncertainty metrics to select valuable samples for the ultimate purpose of route planning during subsequent batch selection. Empirical results demonstrate that our approach significantly surpasses traditional active learning methods. Remarkably, our method achieves comparable results to state-of-the-art end-to-end AD methods - by using only 30% data in both open-loop nuScenes and closed-loop CARLA evaluation.

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

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ACoT-VLA: Action Chain-of-Thought for Vision-Language-Action Models

Zhong, Linqing, Liu, Yi, Wei, Yifei, Xiong, Ziyu, Liu, Si, Ren, Guanghui

原文摘要

Vision-Language-Action models have emerged as essential generalist robot policies for diverse manipulation tasks, conventionally relying on directly translating multimodal inputs into actions via Vision-Language Model embeddings. Recent advancements have introduced explicit intermediary reasoning--such as sub-task prediction (language) or goal image synthesis (vision)--to guide action generation. However, these intermediate reasoning are often indirect and inherently limited in their capacity to convey the full, granular information required for precise action execution. Instead, we posit that the most effective form of reasoning is one that deliberates directly in the action space. We introduce Action Chain-of-Thought (ACoT), a paradigm where the reasoning process itself is formulated as a structured sequence of coarse action intents that guide the final policy. In this paper, we propose ACoT-VLA, a novel architecture that materializes the ACoT paradigm. Specifically, we introduce two complementary components: an Explicit Action Reasoner (EAR) and Implicit Action Reasoner (IAR). The former proposes coarse reference trajectories as explicit action-level reasoning steps, while the latter extracts latent action priors from internal representations of multimodal input, co-forming an ACoT that conditions the downstream action head to enable grounded policy learning. Extensive experiments in real-world and simulation environments demonstrate the superiority of our proposed method. Code is available at: https://github.com/AgibotTech/ACoT-VLA.

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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CrossVL: Complexity-Aware Feature Routing and Paired Curriculum for Cross-View Vision-Language Detection

Liu, Zhipeng, Luo, Chunbo

原文摘要

Vision-language models (VLMs) enable text-guided object detection but degrade severely under cross-view scenarios where ground and aerial viewpoints differ in altitude, scale, and spatial layout. These geometric changes introduce systematic complexity variations between viewpoints, e.g., ground view images contain dense and highly occluded structures, while aerial images are sparse and globally organized. Fixed VLM fusion mechanisms cannot handle this discrepancy. We propose CrossVL, a framework combining Complexity-Aware Pathway Aggregation (CPA) and Paired Curriculum Learning (PCL) for enhanced cross-view detection for VLM. CPA estimates scene complexity from multimodal statistics and routes visual features through multiple pathways to obtain view-specific representations. PCL leverages semantic consistency of synchronized ground-aerial pairs to provide stable early supervision and then gradually shifts toward randomized sampling. On MAVREC, CrossVL improves Florence-2's aerial mAP from 58.66% to 61.03% and reduces the ground-aerial performance gap from 8.63pp to 6.65pp, while also achieving a 3.3x reduction in variance across random seeds. CPA provides stable complexity-aware feature aggregation, and PCL enhances optimization dynamics. Together, they demonstrate that coordinated architectural and training adaptations are crucial for robust cross-view VLM detection.

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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$\alpha$Matte4K & $\mu$Matting: Dataset and Model for Ultra-Micro Precision Alpha Video Matting

Chen, Xinyi, Dong, Hang, Jiang, Baowei, Xu, Shenkun, Guan, Youqi, Shi, Kanle, Gai, Kun, Song, Haichuan

原文摘要

High-resolution human video matting aims to predict accurate alpha mattes for semi-transparent regions while ensuring temporal consistency across frames. Despite notable progress, current methods still fail to achieve a satisfactory trade-off between quality and efficiency, with limitations in subject stability, temporal modeling, and computational cost. In this paper, we introduce \muMatting, an innovative resolution-agnostic two-stage framework for video matting: (1) coarse matte localization using a portrait-aware masked autoencoder; (2) refinement of critical regions via sparse 3D convolution, augmented by a temporal modulator that injects global spatio-temporal cues for enhanced consistency and contextual awareness. From data perspective, existing research remains limited by the insufficient quality of datasets, including (1) inaccurate alpha fractional values resulting from imperfect annotation, and (2) visual inconsistencies arising from arbitrary foreground-background compositions that lack natural coherence. To address this, we introduce \alphaMatte4K, a large-scale 4K-resolution human video matting dataset, which achieves accurate annotations and physical consistency through physically based rendering (PBR). Extensive experiments show that \muMatting surpasses state-of-the-art methods in accuracy and spatio-temporal consistency, while \alphaMatte4K boosts baseline performance, driving applications in real-world scenarios. The project is open-sourced at https://github.com/kadatec/mu-Matting.

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

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Learned Image Compression via Sparse Attention and Adaptive Frequency

Ma, Huidong, Shi, Xinyan, Sun, Hui, Yue, Xiaofei, Liu, Xiaoguang, Wang, Gang, Cai, Wentong

原文摘要

Learned image compression (LIC) methods surpass traditional algorithms in rate-distortion (RD) performance, but still struggle to optimally balance effectiveness and efficiency. Moreover, although recent studies have demonstrated the effectiveness of utilizing frequency-domain information, they typically rely on fixed frequency transforms and still lack content-adaptive capabilities. Therefore, we propose an advanced spatial-frequency dual-path LIC method with enhanced adaptivity and efficiency. Specifically, for the spatial path, we introduce Cross-Sparse Window Attention, leveraging sparse, window-conditioned global tokens to efficiently model long-range dependencies. It achieves lower computational cost and superior effectiveness than standard Window-based Multi-head Self-attention. For the frequency path, we design a content-adaptive frequency transform, employing a decomposition weight generator and learnable global weights to adaptively process multi-scale frequency components. Furthermore, we propose Denoising-as-Regularizer, a training-only module that structures and smooths the latent representation via a denoising task, enhancing reconstruction quality at zero inference cost. Experiments on the Kodak, CLIC, and Tecnick datasets demonstrate that the proposed method significantly outperforms existing state-of-the-art methods in both RD performance and latency.

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

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Real2Edit2Real: Generating Robotic Demonstrations via a 3D Control Interface

Zhao, Yujie, Fan, Hongwei, Chen, Di, Chen, Shengcong, Chen, Liliang, Li, Xiaoqi, Ren, Guanghui, Dong, Hao

原文摘要

Recent progress in robot learning has been driven by large-scale datasets and powerful visuomotor policy architectures, yet policy robustness remains limited by the substantial cost of collecting diverse demonstrations, particularly for spatial generalization in manipulation tasks. To reduce repetitive data collection, we present Real2Edit2Real, a framework that generates new demonstrations by bridging 3D editability with 2D visual data through a 3D control interface. Our approach first reconstructs scene geometry from multi-view RGB observations with a metric-scale 3D reconstruction model. Based on the reconstructed geometry, we perform depth-reliable 3D editing on point clouds to generate new manipulation trajectories while geometrically correcting the robot poses to recover physically consistent depth, which serves as a reliable condition for synthesizing new demonstrations. Finally, we propose a multi-conditional video generation model guided by depth as the primary control signal, together with action, edge, and ray maps, to synthesize spatially augmented multi-view manipulation videos. Experiments on four real-world manipulation tasks demonstrate that policies trained on data generated from only 1-5 source demonstrations can match or outperform those trained on 50 real-world demonstrations, improving data efficiency by up to 10-50x. Moreover, experimental results on height and texture editing demonstrate the framework's flexibility and extensibility, indicating its potential to serve as a unified data generation framework.

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

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Lyapunov Probes for Hallucination Detection in Large Foundation Models

Luan, Bozhi, Li, Gen, Qin, Yalan, Guo, Jifeng, Zhou, Yun, Wu, Faguo, Zheng, Hongwei, Wu, Wenjun 等 9 位作者

原文摘要

We address hallucination detection in Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) by framing the problem through the lens of dynamical systems stability theory. Rather than treating hallucination as a straightforward classification task, we conceptualize (M)LLMs as dynamical systems, where factual knowledge is represented by stable equilibrium points within the representation space. Our main insight is that hallucinations tend to arise at the boundaries of knowledge--transition regions separating stable and unstable zones. To capture this phenomenon, we propose Lyapunov Probes: lightweight networks trained with derivative-based stability constraints that enforce a monotonic decay in confidence under input perturbations. By performing systematic perturbation analysis and applying a two-stage training process, these probes reliably distinguish between stable factual regions and unstable, hallucination-prone areas. Experiments on diverse datasets and models demonstrate consistent improvements over existing baselines.

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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Matching Every Pair to Track Every Point: PairFormer for All-Pairs Tracking and Video Trajectory Fields

Wu, Guangyang, Ding, Youran, Che, Xinyu, Sun, Benyuan, Yang, Yi, Liu, Xiaohong

原文摘要

Tracking-any-point (TAP) answers query-conditioned correspondence but leaves the dense, all-pairs structure of a video implicit. We formulate All-Pairs Tracking (APT): given a video, predict dense displacement and visibility for every source-target frame pair, from which per-pixel trajectories can be read out. To this end, we propose PairFormer, a feed-forward transformer that addresses APT in a single pass. A spatio-temporal patch encoder computes temporally conditioned features for all frames. \revaddtwo CorrBank builds a learnable correlation bank for each frame pair and produces pairwise motion tokens. A broadcast motion mixer aggregates trajectory-wise context and broadcasts it back to refine the pairwise motion tokens. A trajectory head first predicts coarse dense displacement, visibility, and confidence, and then refines them iteratively to form a coherent all-pairs trajectory field. To support APT at scale, we develop PAIRender, a data platform that synthesizes photo-realistic dynamic scenes with dense annotations. From PAIRender we derive a training set (\pi-R10K) and a benchmark (APT-Bench) with an all-to-all evaluation protocol. Experiments show that PairFormer achieves strong performance on APT-Bench and competitive results on standard TAP benchmarks. Code and dataset will be released upon publication.

cs.CV

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

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InternVideo-Next: Towards World-Understanding Video Models

Wang, Chenting, Zhu, Yuhan, Xu, Yicheng, Yang, Jiange, Yan, Ziang, Wang, Yali, Wang, Yi, Wang, Limin

原文摘要

Large-scale video-text pretraining achieves strong performance but depends on noisy, synthetic captions with limited semantic coverage, often overlooking implicit world knowledge such as object motion, 3D geometry, and physical cues. In contrast, masked video modeling (MVM) directly exploits spatiotemporal structures but trails text-supervised methods on general tasks.We find this gap arises from overlooked architectural issues: pixel-level reconstruction struggles with convergence and its low-level requirement often conflicts with semantics, while latent prediction often encourages shortcut learning.To address these, we disentangle the traditional encoder-decoder design into an Encoder-Predictor-Decoder (EPD) framework, where the predictor acts as a latent world model, and propose InternVideo-Next, a two-stage pretraining scheme that builds a semantically consistent yet detail-preserving latent space for this world model.First, conventional linear decoder in pixel MVM enforces the predictor's output latent to be linearly projected to, thus separable in pixel space, causing the conflict with semantic abstraction.Our Stage 1 proposes a conditional diffusion decoder and injects clean image-level semantic priors to enhance semantics and convergence, thus bridging pixel-level fidelity with high-level semantic abstraction.Stage 2 further learns world knowledge by predicting frozen Stage 1 targets within this space, mitigating shortcut learning.Trained on public, unlabeled videos, InternVideo-Next achieves state-of-the-art results across benchmarks and provides a scalable path toward general video representation learning.

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

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Modeling the Visual Ambiguity of Human Sketches

Zhou, Yang, Ni, Ping, Wang, Jin, Jia, Senyun, Yan, Jingdan, Huang, Kaixiang, Lu, Guodong, Yang, Jingru 等 9 位作者

原文摘要

Human sketches provide a compact and expressive form of visual communication, but their sparse structural cues, while capturing essential object structures, introduce ambiguity because a single sketch can correspond to multiple plausible images, making cross-domain alignment uncertain and unstable. Such ambiguity fundamentally limits sketch-based vision tasks that rely on precise sketch--image correspondence. To address this challenge, we introduce AmbiScore, a metric that quantifies the ambiguity of sketch-image pairs, and use Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) as a testbed to reveal how ambiguous supervision leads to performance collapse in existing methods. We further propose DisAmb (Disentangling Ambiguity), a framework that explicitly models and mitigates ambiguity through two components: (1) Elastic Matching, which adaptively adjusts supervision strength using AmbiScore, and (2) Purified Matching, which employs ambiguity-agnostic masks to disentangle structure and appearance via shape jigsaw and texture swapping. DisAmb establishes new benchmarks under high ambiguity and provides a robust, transferable supervisory signal for downstream sketch-guided tasks.

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

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VINS-120K: Ultra High-Resolution Image Editing with A Large-Scale Dataset

Chen, Zhizhou, Guan, Shanyan, Gao, Zhanxin, Ci, En, Ge, Yanhao, Li, Wei, Zhang, Zhenyu, Yang, Jian 等 9 位作者

原文摘要

Directly editing ultra-high-resolution (UHR) images is valuable but underexplored, primarily due to the lack of high-quality data and the challenge in modeling high-frequency texture details. We introduce VINS-120K, the first large-scale dataset for instruction-based UHR image editing, comprising 120K carefully curated triplets of instruction, input image, and edited image. Each image exceeds 4K resolution (\geq4096x4096) and is filtered through a rigorous multi-stage pipeline to ensure visual quality, instruction alignment, and aesthetic fidelity. Built on VINS-120K, we further develop a high-frequency-aware post-adaptation strategy to extend pretrained non-high-resolution models to the UHR regime. We also present VINS-4KEval, a benchmark covering diverse editing types, to facilitate consistent evaluation in UHR settings. Experiments confirm that our work improves fine-grained detail synthesis and texture realism in UHR image editing.

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

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