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首次观测 · 记录 #1CVF · CVPR 2026:cvf:64df97165f650c165d873ae2aa128751CVF · CVPR 2026

Physics-Consistent Diffusion for Efficient Fluid Super-Resolution via Multiscale Residual Correction

Li, Zhihao, Dong, Shengwei, Yi, Chuang, Gao, Junxuan, Lai, Zhilu, Liu, Zhiqiang, Wang, Wei, Zhang, Guangtao

原文摘要

Existing image SR and generic diffusion models transfer poorly to fluid SR: they are sampling-intensive, ignore physical constraints, and often yield spectral mismatch and spurious divergence. We address fluid super-resolution (SR) with **ReMD** (**Re**sidual-**M**ultigrid **D**iffusion), a physics-consistent diffusion framework. At each reverse step, ReMD performs a **multigrid residual correction**: the update direction is obtained by coupling data consistency with lightweight physics cues and then correcting the residual across scales; the multiscale hierarchy is instantiated with a **multi-wavelet** basis to capture both large structures and fine vortical details. This coarse-to-fine design accelerates convergence and preserves fine structures while remaining equation-free. Across atmospheric and oceanic benchmarks, ReMD improves accuracy and spectral fidelity, reduces divergence, and reaches comparable quality with markedly fewer sampling steps than diffusion baselines. Our results show that enforcing physics consistency **inside** the diffusion process via multigrid residual correction and multi-wavelet multiscale modeling is an effective route to efficient fluid SR.

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:6504528b5d2819ebf7b5c5768722353dCVF · CVPR 2026

Computation and Communication Efficient Federated Unlearning via On-server Gradient Conflict Mitigation and Expression

Nguyen, Minh-Duong, Wanasekara, Senura, Nguyen, Le-Tuan, Pham, Quoc-Viet, Yong, Ken-Tye, Tran, Nguyen H., Le, Dung D.

原文摘要

Federated Unlearning (FUL) aims to remove specific participants' data contributions from a trained Federated Learning model, thereby ensuring data privacy and compliance with regulatory requirements. Despite its potential, progress in FUL has been limited due to several challenges, including the cross-client knowledge inaccessibility and high computational and communication costs. To overcome these challenges, we propose Federated On-server Unlearning (FOUL), a novel framework that comprises two key stages. The learning-to-unlearn stage serves as a preparatory learning phase, during which the model identifies and encodes the key features associated with the forget clients. This stage is communication-efficient and establishes the basis for the subsequent unlearning process. Subsequently, on-server knowledge aggregation phase aims to perform the unlearning process at the server without requiring access to client data, thereby preserving both efficiency and privacy. We introduce a new data setting for FUL, which enables a more transparent and rigorous evaluation of unlearning. To highlight the effectiveness of our approach, we propose a novel evaluation metric termed time-to-forget, which measures how quickly the model achieves optimal unlearning performance. Extensive experiments conducted on three datasets under various unlearning scenarios demonstrate that FOUL outperforms the Retraining in FUL. Moreover, FOUL achieves competitive or superior results with significantly reduced time-to-forget, while maintaining low communication and computation costs.

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:65081fca3eab0ce6eb11f691a852bc73CVF · CVPR 2026

Deformation-based In-Context Learning for Point Cloud Understanding

Lin, Chengxing, Deng, Jinhong, Lei, Yinjie, Li, Wen

原文摘要

Recent advances in point cloud In-Context Learning (ICL) have demonstrated strong multitask capabilities. Existing approaches typically adopt a Masked Point Modeling (MPM)-based paradigm for point cloud ICL. However, MPM-based methods directly predict the target point cloud from masked tokens without leveraging geometric priors, requiring the model to infer spatial structure and geometric details solely from token-level correlations via transformers. Additionally, these methods suffer from a training-inference objective mismatch, as the model learns to predict the target point cloud using target-side information that is unavailable at inference time. To address these challenges, we propose DeformPIC, a deformation-based framework for point cloud ICL. Unlike existing approaches that rely on masked reconstruction, DeformPIC learns to deform the query point cloud under task-specific guidance from prompts, enabling explicit geometric reasoning and consistent objectives. Extensive experiments demonstrate that DeformPIC consistently outperforms previous state-of-the-art methods, achieving reductions of 1.6, 1.8, and 4.7 points in average Chamfer Distance on reconstruction, denoising, and registration tasks, respectively. Furthermore, we introduce a new out-of-domain benchmark to evaluate generalization across unseen data distributions, where DeformPIC achieves state-of-the-art performance.

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:65177ac4b781c02c1812598fd4ab1bfeCVF · CVPR 2026

Physical Object Understanding with a Physically Controllable World Model

Venkatesh, Rahul, Kotar, Klemen, Chen, Lilian Naing, Lee, Wanhee, Ancone, Gia, Kim, Seungwoo, Wheeler, Luca Thomas, Watrous, Jared 等 12 位作者

原文摘要

A central challenge in visual intelligence is learning the physical structure of scenes from raw videos: how regions form objects and the laws that govern their interactions. Solving these tasks requires world models capable of inferring distributional states of the world from partial observations -- capabilities that current architectures do not provide. We introduce a new class of probabilistic world models that support estimation of the probability of any visual variable, such as appearance and dynamics, conditioned on any other variables. Here, we identify that these models can be trained efficiently with autoregressive sequence modeling, yielding world models from which rich object understanding emerges. First, we demonstrate that our model captures the physical laws governing how objects move by generating multiple plausible future states of the world through sequential inference. Then, by analyzing motion correlations across these futures, we extract objects and articulated object subparts. Having discovered these objects, we show that our world model can manipulate them in 3D. Finally, we demonstrate how physical relationships between objects can be computed from the world model, enabling applications such as Visual Jenga.

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:6526abc9ea1aa1e965805e87ab11ca0bCVF · CVPR 2026

TAR: Token-Aware Refinement for Fine-grained Generalized Category Discovery

Yang, Xingyu, Zhang, Yu, Mi, Siya, Wei, Xiu-Shen

原文摘要

For an unlabeled dataset containing both known and unknown categories, Generalized Category Discovery (GCD) aims to classify known categories while discovering unknown ones. Although existing methods achieve promising results on coarse-grained datasets, they struggle in fine-grained scenarios. We observe that attention artifacts where attention maps exhibit abnormally high responses on a few tokens--significantly interfere with fine-grained GCD by causing the model to overemphasize global semantics and overlook discriminative local cues. To address this issue, we propose the Token-Aware Refinement (TAR) framework, a plug-and-play module that mitigates attention artifacts and enhances local information. Unlike conventional approaches that rely solely on the first token for classification, TAR leverages the entire token sequence to better capture fine-grained details. Extensive experiments demonstrate that TAR consistently improves performance across multiple benchmarks. Our code is available at https://github.com/VectorYangYiStar/TAR.

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:6528d938d442c0e78c2d9131247c819eCVF · CVPR 2026

SABER: Spatially Consistent 3D Universal Adversarial Objects for BEV Detectors

Li, Aixuan, Xiang, Mochu, Hou, Bosen, Wan, Zhexiong, Zhang, Jing, Dai, Yuchao

原文摘要

Adversarial robustness of BEV 3D object detectors is critical for autonomous driving (AD). Existing invasive attacks require altering the target vehicle itself (e.g. attaching patches), making them unrealistic and impractical for real-world evaluation. While non-invasive attacks that place adversarial objects in the environment are more practical, current methods still lack the multi-view and temporal consistency needed for physically plausible threats. In this paper, we present the first framework for generating universal, non-invasive, and 3D consistent adversarial objects that expose fundamental vulnerabilities for BEV 3D object detectors. Instead of modifying target vehicles, our method inserts rendered objects into scenes with an occlusion-aware module that enforces physical plausibility across views and time. To maintain attack effectiveness across views and frames, we optimize adversarial object appearance using a BEV spatial feature-guided optimization strategy that attacks the detector's internal representations. Extensive experiments demonstrate that our learned universal adversarial objects can consistently degrade multiple BEV detectors from various viewpoints and distances. More importantly, the new environment-manipulation attack paradigm exposes models' over-reliance on contextual cues and provides a practical pipeline for robustness evaluation in AD systems.

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:652a4202265170e800c17992824b6ddeCVF · CVPR 2026

Neural Collapse in Test-Time Adaptation

Chen, Xiao, Du, Zhongjing, Huang, Jiazhen, Jiang, Xu, Lu, Li, Jiang, Jingyan, Wang, Zhi

原文摘要

Test-Time Adaptation (TTA) enhances model robustness to out-of-distribution (OOD) data by updating the model online during inference, yet existing methods lack theoretical insights into the fundamental causes of performance degradation under domain shifts. Recently, Neural Collapse (NC) has been proposed as an emergent geometric property of deep neural networks (DNNs), providing valuable insights for TTA. In this work, we extend NC to the sample-wise level and discover a novel phenomenon termed Sample-wise Alignment Collapse (NC3+), demonstrating that a sample's feature embedding, obtained by a trained model, aligns closely with the corresponding classifier weight. Building on NC3+, we identify that the performance degradation stems from sample-wise misalignment in adaptation which exacerbates under larger distribution shifts. This indicates the necessity of realigning the feature embeddings with their corresponding classifier weights. However, the misalignment makes pseudo-labels unreliable under domain shifts. To address this challenge, we propose NCTTA, a novel feature-classifier alignment method with hybrid targets to mitigate the impact of unreliable pseudo-labels, which blends geometric proximity with predictive confidence. Extensive experiments demonstrate the effectiveness of NCTTA in enhancing robustness to domain shifts. For example, NCTTA outperforms Tent by 14.52% on ImageNet-C. Project page is publicly available at https://github.com/Cevaaa/NCTTA.

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:65336a755540a6da8a1e25406245ef00CVF · CVPR 2026

MoLingo: Motion-Language Alignment for Text-to-Human Motion Generation

He, Yannan, Tiwari, Garvita, Zhang, Xiaohan, Bora, Pankaj, Birdal, Tolga, Lenssen, Jan Eric, Pons-Moll, Gerard

原文摘要

We introduce MoLingo, a text-to-motion (T2M) model that generates realistic, lifelike human motion by denoising in a continuous latent space. Recent works perform latent space diffusion, either on the whole latent at once or auto-regressively over multiple latents. In this paper, we study how to make diffusion on continuous motion latents work best. We focus on two questions: (1) how to build a semantically aligned latent space so diffusion becomes more effective, and (2) how to best inject text conditioning so the motion follows the description closely. We propose a semantic-aligned motion encoder trained with frame-level text labels so that latents with similar text meaning stay close, which makes the latent space more diffusion-friendly. We also compare single-token conditioning with a multi-token cross-attention scheme and find that cross-attention gives better motion realism and text-motion alignment. With semantically aligned latents, auto-regressive generation, and cross-attention text conditioning, our model sets a new state-of-the-art in human motion generation on standard metrics and in a user study. We will release our code and models for further research and downstream usage.

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:657710cedd89e16a09ad3fe6c3c5dd9fCVF · CVPR 2026

Event Stream Filtering via Probability Flux Estimation

Chen, Jinze, Zhai, Wei, Cao, Yang, Li, Bin, Zha, Zheng-Jun

原文摘要

Event cameras asynchronously capture brightness changes with microsecond latency, offering exceptional temporal precision but suffering from severe noise and signal inconsistencies. Unlike conventional signals, events carry state information through polarities and process information through inter-event time intervals. However, existing event filters often ignore the latter, producing outputs that are sparser than the raw input and limiting the reconstruction of continuous irradiance dynamics. We propose the Event Density Flow Filter (EDFilter), a framework that models event generation as threshold-crossing probability fluxes arising from the stochastic diffusion of irradiance trajectories. EDFilter performs nonparametric, kernel-based estimation of probability flux and reconstructs the continuous event density flow using an O(1) recursive solver, enabling real-time processing. The Rotary Event Dataset (RED), featuring microsecond-resolution ground-truth irradiance flow under controlled illumination is also presented for event quality evaluation. Experiments demonstrate that EDFilter achieves high-fidelity, physically interpretable event denoising and motion reconstruction.

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:658192410a1e3192d8f2c58afe699f4eCVF · CVPR 2026

ReBaPL: Repulsive Bayesian Prompt Learning

Bendou, Yassir, Ezzahir, Omar, Montesuma, Eduardo, Mahuas, Gabriel, Shevchenko, Victoria, Gartrell, Mike

原文摘要

Prompt learning has emerged as an effective technique for fine-tuning large-scale foundation models for downstream tasks. However, conventional prompt learning methods are prone to overfitting and can struggle with out-of-distribution generalization. To address these limitations, Bayesian prompt learning has been proposed, which frames prompt optimization as a Bayesian inference problem to enhance robustness. This paper introduces Repulsive Bayesian Prompt Learning (ReBaPL), a novel method for Bayesian prompt learning, designed to efficiently explore the complex and often multimodal posterior landscape of prompts. Our method integrates a cyclical step-size schedule with a stochastic gradient Hamiltonian Monte Carlo (SGHMC) algorithm, enabling alternating phases of exploration to discover new modes, and exploitation to refine existing modes. Furthermore, we introduce a repulsive force derived from a potential function over probability metrics (including Maximum Mean Discrepancy and Wasserstein distance) computed on the distributions of representations produced by different prompts. This representation-space repulsion diversifies exploration and prevents premature collapse to a single mode. Our approach allows for a more comprehensive characterization of the prompt posterior distribution, leading to improved generalization. In contrast to prior Bayesian prompt learning methods, our method provides a modular plug-and-play Bayesian extension of any existing prompt learning method based on maximum likelihood estimation. We demonstrate the efficacy of ReBaPL on several benchmark datasets, showing superior performance over state-of-the-art prompt learning methods.

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:659497ff7130a592f1ab2589561e2b42CVF · CVPR 2026

SafeGRPO: Self-Rewarded Multimodal Safety Alignment via Rule-Governed Policy Optimization

Rong, Xuankun, Huang, Wenke, Wang, Tingfeng, Zhou, Daiguo, Du, Bo, Ye, Mang

原文摘要

Multimodal large language models (MLLMs) have demonstrated impressive reasoning and instruction-following capabilities, yet their expanded modality space introduces new compositional safety risks that emerge from complex text-image interactions.Such cross-modal couplings can produce unsafe semantics even when individual inputs are benign, exposing the fragile safety awareness of current MLLMs.While recent works enhance safety by guiding models to reason about potential risks, unregulated reasoning traces may compromise alignment; although Group Relative Policy Optimization (GRPO) offers self-rewarded refinement without human supervision, it lacks verifiable signals for reasoning safety.To address this, we propose **SafeGRPO** a self-rewarded multimodal safety alignment framework that integrates rule-governed reward construction into GRPO, enabling interpretable and verifiable optimization of reasoning safety. Built upon the constructed **SafeTag-VL-3K** dataset with explicit visual, textual, and combined safety tags, SafeGRPO performs **step-guided safety thinking** to enforce structured reasoning and behavior alignment, substantially improving multimodal safety awareness, compositional robustness, and reasoning stability across diverse benchmarks without sacrificing general capabilities.

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:6598b405244e63adddc955dee788b954CVF · CVPR 2026

Enhancing Descriptive Captions with Visual Attributes for Multimodal Perception

Sun, Yanpeng, Hao, Jing, Zhu, Ke, Liu, Jiang-Jiang, Li, Xiaofan, Zhao, Na, Li, Zechao, Wang, Jingdong

原文摘要

Training Large Multimodality Models (LMMs) relies on descriptive image caption that connects image and language. Existing methods for generating such captions often rely on distilling the captions from pretrained LMMs, constructing them from publicly available internet images, or even generating them through human annotation. However, these strategies can fall short in terms of precision and granularity, particularly when dealing with complex visual reasoning tasks. In this paper, we propose to leverage off-the-shelf visual specialists, which were trained from annotated images initially not for image captioning, for enhancing the image caption. Our approach, named Cap-Workflow, explores object low-level and fine-grained attributes (e.g., depth, emotion and fine-grained categories) and object relations (e.g., relative location and human-object-interaction (HOI)), and combine the attributes into the descriptive caption. By systematically integrating these rich attributes into the generated captions, Cap-Workflow significantly improves the descriptive quality of the captions, providing a deeper and more nuanced understanding of the visual content. Experiments demonstrate that such visual specialists are able to improve the performance for visual understanding tasks as well as reasoning that benefits from more accurate visual understanding. The source code and datasets are available at https://github.com/syp2ysy/Cap-Workflow.

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:659c386b7ccc1866be8b15e56ceb2e15CVF · CVPR 2026

Edit-aware RAW reconstruction

Punnappurath, Abhijith, Zhao, Luxi, Zhao, Ke, Nguyen, Hue, Grzeszczuk, Radek, Brown, Michael S.

原文摘要

Users frequently edit camera images post-capture to achieve their preferred photofinishing style. While editing in the RAW domain provides greater accuracy and flexibility, most edits are performed on the camera's display-referred output (e.g., 8-bit sRGB JPEG) since RAW images are rarely stored. Existing RAW reconstruction methods can recover RAW data from sRGB images, but these approaches are typically optimized for pixel-wise RAW reconstruction fidelity and tend to degrade under diverse rendering styles and editing operations. We introduce a plug-and-play, edit-aware loss function that can be integrated into any existing RAW reconstruction framework to make the recovered RAWs more robust to different rendering styles and edits. Our loss formulation incorporates a modular, differentiable image signal processor (ISP) that simulates realistic photofinishing pipelines with tunable parameters. During training, parameters for each ISP module are randomly sampled from carefully designed distributions that model practical variations in real camera processing. The loss is then computed in sRGB space between ground-truth and reconstructed RAWs rendered through this differentiable ISP. Incorporating our loss improves sRGB reconstruction quality by up to 1.5-2 dB PSNR across various editing conditions. Moreover, when applied to metadata-assisted RAW reconstruction methods, our approach enables fine-tuning for target edits, yielding further gains. Since photographic editing is the primary motivation for RAW reconstruction in consumer imaging, our simple yet effective loss function provides a general mechanism for enhancing edit fidelity and rendering flexibility across 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:659d8d5d7fc97b0bb19c2ede7b06e5f2CVF · CVPR 2026

Mind the Generative Details: Direct Localized Detail Preference Optimization for Video Diffusion Models

Huang, Zitong, Zhang, Kaidong, Ding, Yukang, Gao, Chao, Ding, Rui, Chen, Ying, Zuo, Wangmeng

原文摘要

Aligning text-to-video diffusion models with human preferences is crucial for generating high-quality videos. Existing Direct Preference Otimization (DPO) methods rely on multi-sample ranking and task-specific critic models, which is inefficient and often yields ambiguous global supervision. To address these limitations, we propose LocalDPO, a novel post-training framework that constructs localized preference pairs from real videos and optimizes alignment at the spatio-temporal region level. We design an automated pipeline to efficiently collect preference pair data that generates preference pairs with a single inference per prompt, eliminating the need for external critic models or manual annotation. Specifically, we treat high-quality real videos as positive samples and generate corresponding negatives by locally corrupting them with random spatio-temporal masks and inpainting only the masked regions using the frozen base model. During training, we introduce a region-aware DPO loss that restricts preference learning to corrupted areas for rapid convergence. Experiments on Wan2.1 and CogVideoX demonstrate that LocalDPO consistently improves video fidelity, temporal coherence and human preference scores over other post-training approaches, establishing a more efficient and fine-grained paradigm for video generator alignment.

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:65aee107088aa80fbd1279fb642b7449CVF · CVPR 2026

TESO: Online Tracking of Essential Matrix by Stochastic Optimization

Moravec, Jaroslav, Sara, Radim, Sugimoto, Akihiro

原文摘要

Maintaining long-term accuracy of stereo camera calibration parameters is important for autonomous systems' perception. This work proposes Online Tracking of Essential Matrix by Stochastic Optimization (TESO). The core mechanisms of TESO are: 1) a robust loss function based on kernel correlation over tentative correspondences, 2) an adaptive online stochastic optimization on the essential manifold. TESO has low CPU and memory requirements, relies on a few hyperparameters, and eliminates the need for data-driven training, enabling the usage in resource-constrained online perception systems. We evaluated the influence of TESO on geometric precision, rectification quality, and stereo depth consistency. On the large-scale MAN TruckScenes dataset, TESO tracks rotational calibration drift with 0.12 deg precision in the Y-axis-critical for stereo accuracy-while the X- and Z-axes are five times more precise. Tracking applied to sequences with simulated drift shows similar precision with respect to the reference as tracking applied to no-drift sequences, indicating the tracker is unbiased. On the KITTI dataset, TESO revealed systematic inconsistencies in extrinsic parameters across stereo pairs, confirming previous published findings. We verified that intrinsic decalibration affected these errors, as evidenced by the conflicting behavior of the rectification and depth metrics. After correcting the reference calibration, TESO improved its rotation precision around the Y-axis 20 times to 0.025 deg and its depth accuracy 50 times. Despite its lightweight design, direct optimization of the proposed TESO loss function alone achieves accuracy comparable to that of neural network-based single-frame 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:65b4d49b3860bbb313ee4ef9a4a13c4aCVF · CVPR 2026

PR-MaGIC: Prompt Refinement Via Mask Decoder Gradient Flow For In-Context Segmentation

Lee, Minjae, Hur, Sungwoo, Hwang, Soojin, Kim, Won Hwa

原文摘要

Visual Foundation Models (VFMs) such as the Segment Anything Model (SAM) have significantly advanced broad use of image segmentation. However, SAM and its variants necessitate substantial manual effort for prompt generation and additional training for specific applications. Recent approaches address these limitations by integrating SAM into in-context (one/few shot) segmentation, enabling auto-prompting through semantic alignment between query and support images. Despite these efforts, they still generate sub-optimal prompts that degrade segmentation quality due to visual inconsistencies between support and query images. To tackle this limitation, we introduce PR-MaGIC (Prompt Refinement via Mask Decoder Gradient Flow for In-Context Segmentation), a training-free test-time framework that refines prompts via gradient flow derived from SAM's mask decoder. PR-MaGIC seamlessly integrates into in-context segmentation frameworks, being theoretically grounded yet practically stabilized through a simple top-1 selection strategy that ensures robust performance across samples. Extensive evaluations demonstrate that PR-MaGIC consistently improves segmentation quality across various benchmarks, effectively mitigating inadequate prompts without requiring additional training or architectural modifications.

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:65c0a7b1db98afa9bdd591048973d140CVF · CVPR 2026

IMAIA: Interactive Maps AI Assistant for Travel Planning and Geo-Spatial Intelligence

Deng, Jieren, Hu, Zhizhang, He, Ziyan, Cvetkovic, Aleksandar, Chung, Pak Kiu, Yankov, Dragomir, Zhang, Chiqun

原文摘要

Modern mapping tools remain fundamentally point-and-click systems, offering limited support for interactive, multimodal engagement with either map views or real-world camera inputs. This limitation becomes most apparent in the last 100 meters of navigation and during in-situ local discovery, where users naturally combine spatial reasoning over maps with egocentric perception of their surroundings. We introduce IMAIA, an interactive geospatial assistance framework that unifies three core capabilities: (1) map-centric spatial understanding, (2) camera-to-place grounding, and (3) human-centered, bearing-aware navigation. IMAIA consists of two interoperable components--Maps Plus and PAISA--coordinated by a lightweight multi-agent orchestrator. Maps Plus converts vector and satellite maps into a grid-aligned representation that enables flexible, view-conditioned spatial queries. PAISA fuses camera imagery with geospatial signals such as location, heading, and proximity to interpret egocentric scenes and surface relevant attributes of nearby places. The framework is fully modular: vision-language backends can be replaced without altering system behavior. IMAIA enables responsive, interpretable, and context-aware assistance across both map-based and real-world interactions, supporting a new class of interactive mapping experiences that are practical for real-world deployment.

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:65d649aaeb086536fb485407d37dc2f2CVF · CVPR 2026

Grounded Chain-of-Thought for Multimodal Large Language Models

Wu, Qiong, Yang, Xiangcong, Zhou, Yiyi, Fang, Chenxin, Song, Baiyang, Sun, Xiaoshuai, Ji, Rongrong

原文摘要

Despite great progress, existing multimodal large language models (MLLMs) are still inferior in visual-spatial reasoning, which greatly impedes their trustworthy applications in scenarios such as Embodied AI. To facilitate the research, we propose a new MLLM task in this paper, called Grounded Chain-of-Thought (GCoT). Different from recent visual CoT studies, which focus more on visual knowledge reasoning, GCoT aims to improve the visual-spatial reasoning capabilities of MLLMs via recognizing and grounding the relevant visual cues step by step, which are also supported by step-vise grounding coordinates as the intuitive basis. To facilitate this task, we also carefully design and construct a benchmark called multimodal grounded chain-of-thought (MM-GCoT). Besides, a comprehensive consistency evaluation system is also introduced, including the metrics of answer accuracy, grounding accuracy and answer-grounding consistency. We further design and conduct a bunch of experiments on 12 advanced MLLMs, and reveal some notable findings: i. most MLLMs performs poorly on the consistency evaluation, indicating obvious visual hallucination; ii. visual hallucination is not directly related to the parameter size and general multimodal performance; iii. a larger and stronger MLLM is not less affected by this issue.

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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SpaceTools: Tool-Augmented Spatial Reasoning via Double Interactive RL

Chen, Siyi, Uy, Mikaela Angelina, Song, Chan Hee, Ladhak, Faisal, Murali, Adithyavairavan, Qu, Qing, Birchfield, Stan, Blukis, Valts 等 9 位作者

原文摘要

Vision Language Models (VLMs) demonstrate strong qualitative visual understanding, but struggle with metrically precise spatial reasoning required for embodied applications. The agentic paradigm promises that VLMs can use a wide variety of tools that could augment these capabilities, such as depth estimators, segmentation models, and pose estimators. Yet it remains an open challenge how to realize this vision without solely relying on handcrafted prompting strategies or enforcing fixed, predefined tool pipelines that limit VLMs' ability to discover optimal tool-use patterns. Reinforcement Learning could overcome this gap, but has so far been limited to reasoning with a single visual tool due to the large search space in multi-tool reasoning. We introduce Double Interactive Reinforcement Learning (DIRL), a two-phase training framework where VLMs learn to coordinate multiple tools through interactive exploration and feedback. In the teaching phase, we combine demonstrations from a single tool specialist trained via interactive RL with traces from a frontier model using all tools. In the exploration phase, the model further refines multi-tool coordination through continued RL. Our model, SpaceTools, with tool-augmented spatial reasoning ability, achieves state-of-the-art performance on spatial understanding benchmarks (RoboSpatial-Home, BLINK, Internal Benchmark) and demonstrates reliable real-world manipulation using a 7-DOF robot as a tool. DIRL provides substantial improvements over the vanilla SFT (+12% on RoboSpatial) and RL (+16% on RoboSpatial) baselines. Project page: https://spacetools.github.io/.

cs.CV · cs.CL · cs.AI

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

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Unposed-to-3D: Learning Simulation-Ready Vehicles from Real-World Images

Liu, Hongyuan, Zou, Bochao, Liu, Qiankun, Yu, Haochen, Mei, Qi, Jiang, Jianfei, Liu, Chen, Bi, Cheng 等 13 位作者

原文摘要

Creating realistic and simulation-ready 3D assets is crucial for autonomous driving research and virtual environment construction. However, existing 3D vehicle generation methods are often trained on synthetic data with significant domain gaps from real-world distributions. The generated models often exhibit arbitrary poses and undefined scales, resulting in poor visual consistency when integrated into driving scenes. In this paper, we present Unposed-to-3D, a novel framework that learns to reconstruct 3D vehicles from real-world driving images using image-only supervision. Our approach consists of two stages. In the first stage, we train an image-to-3D reconstruction network using posed images with known camera parameters. In the second stage, we remove camera supervision and use a camera prediction head that directly estimates the camera parameters from unposed images. The predicted pose is then used for differentiable rendering to provide self-supervised photometric feedback, enabling the model to learn 3D geometry purely from unposed images. To ensure simulation readiness, we further introduce a scale-aware module to predict real-world size information, and a harmonization module that adapts the generated vehicles to the target driving scene with consistent lighting and appearance. Extensive experiments demonstrate that Unposed-to-3D effectively reconstructs realistic, pose-consistent, and harmonized 3D vehicle models from real-world images, providing a scalable path toward creating high-quality assets for driving scene simulation and digital twin environments.

cs.CV

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

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R-4B: Incentivizing General-Purpose Auto-Thinking in MLLMs via Bi-Mode Annealing and Reinforce Learning

Yang, Qi, Ni, Bolin, Xiang, Shiming, Peng, Houwen

原文摘要

Multimodal Large Language Models (MLLMs) with explicit step-by-step reasoning have achieved strong performance on complex tasks. However, such reasoning is unnecessary for many simple queries and introduces substantial computational overhead. To address this inefficiency, we present R-4B, an auto-thinking MLLM that dynamically determines whether to invoke the reasoning process based on input complexity.Our key idea is to equip a single model with both thinking and non-thinking capabilities and train it to select the appropriate mode. We first introduce bi-mode annealing, a unified training paradigm that constructs a model competent in both reasoning-intensive and direct-answer settings without requiring explicit complexity annotations. Building on this foundation, we propose Bi-mode Policy Optimization (BPO), a lightweight reinforcement learning algorithm that employs a dual-rollout mechanism: for each input, the model generates both thinking and non-thinking responses. This prevents mode collapse and enables robust learning of an adaptive reasoning policy using only simple, rule-based rewards.Extensive experiments across 25 benchmarks show that R-4B achieves state-of-the-art performance among models of similar scale. It consistently surpasses Qwen2.5-VL-7B and matches or exceeds larger models such as Kimi-VL-A3B-Thinking-2506 (16B) on reasoning-intensive tasks, while reducing computational cost by avoiding redundant reasoning. Our results demonstrate that adaptive auto-thinking offers an effective and scalable pathway toward more efficient multimodal reasoning 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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TROPHIES: Temporal Reconstruction of Places, Humans, and Cameras from Multi-view Videos

Liu, Jinpeng, Xu, Yukang, Li, Yutong, Liu, Xingyu

原文摘要

Reconstructing humans and their surrounding environments in a globally consistent 4D space is essential for comprehensive perception. However, prior works typically assume single-view inputs or decouple humans, scenes, and cameras, making them unable to recover coherent geometry, stable motion, and physically aligned trajectories. These limitations motivate us to introduce a new task: unified human-scene-camera reconstruction from multi-view videos, which aims to jointly estimate dynamic humans, static scenes, and camera poses in one global coordinate frame. We propose TROPHIES--Temporal Reconstruction of Places, Humans, and Cameras from Multi-view Videos--a unified framework tailored for this task. TROPHIES features a Human Branch that models human through temporal and spatial reasoning, and a Scene Branch that reconstructs static geometry with human-aware attention. A global alignment and optimization module couples both branches by enforcing scale consistency, contact priors, and cross-view temporal coherence. Experiments on EgoHuman and EgoExo4D demonstrate that TROPHIES achieves globally aligned, physically plausible 4D reconstructions and consistently outperforms existing paradigms in both global fidelity and human-scene consistency.

cs.CV

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

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DLVP-CLIP: Enhancing Fine-Grained Zero-Shot Anomaly Detection via Dynamic Local Visual Prompting

Zhang, Gaowei, Zhang, Lihe

原文摘要

Zero-shot anomaly detection (ZSAD) aims to utilize auxiliary data to train models for generalized learning of unseen categories, which has important application value in fields such as industrial quality inspection and medical diagnosis. Although methods based on CLIP show potential, their pre-training objective of focusing on overall semantic alignment between images and text makes the model insensitive to local details, which is inherently contradictory to the need for fine-grained local features in anomaly detection. Existing improvement methods rely on predefined text prompt frameworks to perceive local information, but struggle to effectively address the issue of insufficient local perception. To address this, this paper proposes a dynamic local visual prompting method based on CLIP (DLVP-CLIP). DLVP dynamically identifies and extracts local visual features from key regions in images as prompt tokens using the Semantic-Aware Local Feature Selector (SLFS) module, and utilizes the multi-modal local prompt (MLoP) module to jointly optimize representations in both visual and textual spaces, achieving more precise cross-modal alignment. Additionally, the high-low frequency decomposition module (HFD) is introduced to separate and process global structural and local textural information via wavelet transformation, thereby enhancing detail perception. Extensive experiments on 13 anomaly detection datasets demonstrate that DLVP-CLIP achieves outstanding ZSAD performance on datasets from the industrial and medical domains.

cs.CV

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

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2-Shots in the Dark: Low-Light Denoising with Minimal Data Acquisition

Lu, Liying, Achddou, Raphael, Süsstrunk, Sabine

原文摘要

Raw images taken in low-light conditions are very noisy due to low photon count and sensor noise. Learning-based denoisers have the potential to reconstruct high-quality images. For training, however, these denoisers require large paired datasets of clean and noisy images, which are difficult to collect. Noise synthesis is an alternative to large-scale data acquisition: given a clean image, we can synthesize a realistic noisy counterpart. In this work, we propose a general and practical noise synthesis method that requires only one single noisy image and one single dark frame per ISO setting. We represent signal-dependent noise with a Poisson distribution and introduce a Fourier-domain spectral sampling algorithm to accurately model signal-independent noise. The latter generates diverse noise realizations that maintain the spatial and statistical properties of real sensor noise. As opposed to concurrent approaches, our method neither relies on simplified parametric models nor on large sets of clean-noisy image pairs. It is accurate and practical. Moreover, our synthesis method leads to state-of-the-art performances on multiple low-light denoising benchmarks.

cs.CV

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

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Uncertainty-driven 3D Gaussian Splatting Active Mapping via Anisotropic Visibility Field

Xue, Shangjie, Dill, Jesse, Ahuja, Dhruv, Dellaert, Frank, Tsiotras, Panagiotis, Xu, Danfei

原文摘要

We present Gaussian Splatting Anisotropic Visibility Field (GAVIS), a novel framework for uncertainty quantification and active mapping in 3DGS. Our key insight is that regions unseen from the training views yield unreliable predictions from the 3DGS. To address this, we introduce a principled and efficient method for quantifying the visibility field in 3DGS, defined as the anisotropic visibility of each particle with respect to the training views, and represented using spherical harmonics. The resulting visibility field is integrated into a Bayesian Network-based uncertainty-aware 3DGS rasterizer, enabling real-time (200 FPS) uncertainty quantification for synthesized views. Active mapping is further performed within a maximum information gain framework building on this formulation. Extensive experiments across diverse environments demonstrate that GAVIS consistently and significantly outperforms prior approaches in both accuracy and efficiency. Moreover, beyond standalone use, our method can be applied post-hoc to improve the performance of existing approaches.

cs.CV

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

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ReFTA: Breaking the Weight Reconstruction Bottleneck in Tensorized Parameter-Efficient Fine-Tuning

Zheng, Jingjing, Tang, Anda, Mao, Qiangqiang, Lin, Zhouchen, Cao, Yankai

原文摘要

Tensor-based methods have attracted growing interest due to their ability to reduce trainable parameters and offer advantages over matrix-based approaches in parameter-efficient fine-tuning (e.g., LoRA and PiSSA), particularly in capturing inter-layer correlations. However, directly applying tensor methods requires repeated reconstruction of model weights during training, introducing substantial overhead and becoming a key bottleneck for deployment. To address this limitation, we propose Reconstruction-Free Tensor-Based Adaptation (ReFTA), which offers four advantages: (1) eliminating weight tensor reconstruction via tensor algebraic properties; (2) reducing initialization quantization error by fine-tuning only the principal tensor components; (3) providing a rigorous generalization guarantee rooted in tensor product algebra; and (4) enabling a unified design controlled by a single tensor rank configuration. Extensive experiments on both image classification (IC) and natural language understanding (NLU) tasks demonstrate that ReFTA achieves the best accuracy-efficiency trade-off among all evaluated methods. Across most cases, ReFTA attains the highest average accuracy with the fewest trainable parameters. On IC tasks with the ViT-Large model, ReFTA surpasses LoRA by 5.6% in average accuracy, while reducing the number of trainable parameters by up to 96% compared to LoRA. On NLU tasks with RoBERTa-Large, ReFTA improves the average accuracy by approximately 5% over most existing methods while using only 86.4% fewer parameters than LoRA (r=1) and 97.5% fewer than PiSSA. The code is available at https://github.com/jzheng20/ReFTA.

cs.CV

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

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SO(3)-Equivariant ViT-Adapter for Data-Efficient Zero-Shot Sim-to-Real Indoor Panoramic Depth Estimation

He, Ziyan, Zhang, Qiudan, Ma, Lin, Wang, Xu

原文摘要

Panoramic depth estimation enables a complete 360^\circ understanding of 3D environments but faces significant challenges in generalizing to real-world scenes. While recent zero-shot depth models like Depth Anything achieve remarkable generalization on perspective images, their performance sharply degrades on panoramas due to projection distortions and the lack of spherical geometric awareness. Moreover, collecting large-scale panoramic RGB-D data is costly, hindering the large-scale training of panoramic foundation models. To address these issues, we propose an SO(3)-Equivariant ViT-Adapter, which transfers the powerful zero-shot capability of the perspective pre-trained ViT to panoramic depth estimation by explicitly incorporating a rotation-equivariant inductive bias. Our adapter introduces an SO(3) deformable cross-attention mechanism to effectively align SO(3)-equivariant features with perspective features, enhancing rotational consistency without modifying the ViT backbone. Trained solely on synthetic panoramas, our framework achieves robust zero-shot sim-to-real performance on real indoor benchmarks, including Matterport3D and Stanford2D3D, demonstrating both data efficiency and strong generalization for panoramic depth estimation.

cs.CV

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

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Tunable Soft Equivariance with Guarantees

Rahman, Md Ashiqur, Hao, Lim Jun, Jiang, Jeremiah, Lim, Teck-Yian, Yeh, Raymond A.

原文摘要

Equivariance is a fundamental property in computer vision models, yet strict equivariance is rarely satisfied in real-world data, which can limit a model's performance. Controlling the degree of equivariance is therefore desirable. We propose a general framework for constructing soft equivariant models by projecting the model weights into a designed subspace. The method applies to any pre-trained architecture and provides theoretical bounds on the induced equivariance error. Empirically, we demonstrate the effectiveness of our method on multiple pre-trained backbones, including ViT and ResNet, across image classification, semantic segmentation, and human-trajectory prediction tasks. Notably, our approach improves the performance while simultaneously reducing equivariance error on the competitive ImageNet benchmark.

cs.CV

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

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Rejection Mixing: Fast Semantic Propagation of Mask Tokens for Efficient DLLM Inference

Ye, Yushi, Hong, Feng, Zheng, Huangjie, Chen, Xu, Chen, Zhiyong, Wang, Yanfeng, Yao, Jiangchao

原文摘要

Diffusion Large Language Models (DLLMs) promise fast non-autoregressive inference but suffer a severe quality and speed tradeoff in parallel decoding. This stems from the "combinatorial contradiction" phenomenon, where parallel tokens form semantically inconsistent combinations. We address this by integrating continuous representations into the discrete decoding process, as they preserve rich inter-position dependency. We propose ReMix (Rejection Mixing), a framework that introduces a novel Continuous Mixing State as an intermediate between the initial masked state and the final decoded token state. This intermediate state allows a token's representation to be iteratively refined in a continuous space, resolving mutual conflicts with other tokens before collapsing into a final discrete sample. Furthermore, a rejection rule reverts uncertain representations from the continuous state back to the masked state for reprocessing, ensuring stability and preventing error propagation.ReMix thus mitigates combinatorial contradictions by enabling continuous-space refinement during discrete diffusion decoding. Extensive experiments demonstrate that ReMix, as a training-free method, achieves a 2-8xinference speedup without any quality degradation.

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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gQIR: Generative Quanta Image Reconstruction

Garg, Aryan, Ma, Sizhuo, Gupta, Mohit

原文摘要

Capturing high-quality images from only a few detected photons is a fundamental challenge in computational imaging. Single-photon avalanche diode (SPAD) sensors promise high-quality imaging in regimes where conventional cameras fail, but raw quanta frames contain only sparse, noisy, binary photon detections. Recovering a coherent image from a burst of such frames requires handling alignment, denoising, and demosaicing (for color) under noise statistics far outside those assumed by standard restoration pipelines or modern generative models. We present an approach that adapts large text-to-image latent diffusion models to the photon-limited domain of quanta burst imaging. Our method leverages the structural and semantic priors of internet-scale diffusion models while introducing mechanisms to handle Bernoulli photon statistics. By integrating latent-space restoration with burst-level spatio-temporal reasoning, our approach produces reconstructions that are both photometrically faithful and perceptually pleasing, even under high-speed motion. We evaluate the method on synthetic benchmarks and new real-world datasets, including the first color SPAD burst dataset and a challenging Deforming (XD) video benchmark. Across all settings, the approach substantially improves perceptual quality over classical and modern learning-based baselines, demonstrating the promise of adapting large generative priors to extreme photon-limited sensing. Code at https://github.com/Aryan-Garg/gQIR.

cs.CV

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

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