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首次观测 · 记录 #1CVF · CVPR 2026:cvf:32c094bbd888d01f654d8e7fac0b88e4CVF · CVPR 2026

Wavelet-Driven 3D Anomaly Detection under Pose-Agnostic and Sparse-View

Shao, Mingwen, Zhang, Qiao, Chen, Xinyuan, Lv, Xiang, Meng, Lingzhuang, Liu, Chang, Zhan, Qinglin, Jian, Ling

原文摘要

Pose-agnostic anomaly detection (PAD) achieves strong performance in localizing anomalies from arbitrary viewpoints when trained on densely sampled normal data. However, under sparse-view conditions, existing methods face two key challenges: (1) sparse observations lead to overfitting and geometric detail loss in 3D reconstruction; (2) limited visual cues lead to inaccurate pose estimation, compromising the reliability of subsequent anomaly localization. To address these challenges, we propose Wave-Pose3D, a wavelet-driven 3D anomaly detection framework tailored for PAD under sparse-view conditions. First, we design a structure-aware and wavelet-optimized Gaussian modeling strategy that dynamically filters unreliable regions via structural priors to mitigate overfitting and leverages high-frequency supervision to restore fine-grained geometric details. Second, to improve pose estimation under sparse views, we develop a wavelet-based pose estimator that integrates low-frequency structural cues and high-frequency details to enhance both initialization and refinement accuracy. Finally, we introduce a wavelet difference-aware anomaly detector that computes frequency-domain anomaly scores, improving localization robustness against pose and geometric variations. By integrating these strategies, Wave-Pose3D achieves robust and accurate anomaly localization under sparse views. Extensive experiments validate that the proposed approach achieves state-of-the-art performance under 10% and 20% sparse-view configurations.

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:32f1ce8084a4d5f966caa5a5e4b9147eCVF · CVPR 2026

FINER: MLLMs Hallucinate under Fine-grained Negative Queries

Xiao, Rui, Kim, Sanghwan, Xian, Yongqin, Akata, Zeynep, Alaniz, Stephan

原文摘要

Multimodal large language models (MLLMs) struggle with hallucinations, particularly with fine-grained queries, a challenge underrepresented by existing benchmarks that focus on coarse image-related questions. We introduce **FI**ne-grained **NE**gative que**R**ies (**FINER**), alongside two benchmarks: **FINER-CompreCap** and **FINER-DOCCI**. Using FINER, we analyze hallucinations across four settings: multi-object, multi-attribute, multi-relation, and "what" questions. Our benchmarks reveal that MLLMs hallucinate when fine-grained mismatches co-occur with genuinely present elements in the image. To address this, we propose **FINER-Tuning**, leveraging Direct Preference Optimization (DPO) on FINER-inspired data. Finetuning four frontier MLLMs with FINER-Tuning yields up to 24.2% gains (InternVL3.5-14B) on hallucinations from our benchmarks, while simultaneously improving performance on eight existing hallucination suites, and enhancing general multimodal capabilities across six benchmarks. Code, benchmark, and models are available at https://explainableml.github.io/finer-project/.

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:32f50fd50fa3bac6f80d4372b0d5ff9cCVF · CVPR 2026

Hyper-PCN: Hypergraph-Based Point Cloud Completion via High-Order Correlation Modeling

Li, Linfei, Tan, Pei, Li, Siqi, Zou, Changqing, Gao, Yue

原文摘要

Point cloud completion is an important yet challenging problem in 3D computer vision, which aims to reconstruct complete and dense 3D shapes from partial point clouds. Although transformer-based and geometry-based approaches have made significant progress, they often struggle to capture the complex, high-order correlations inherent in point clouds. To address this limitation, we propose Hyper-PCN, a point cloud completion framework that leverages hypergraphs to explicitly model complex, higher-order correlations within incomplete inputs for more accurate completion. It comprises two key modules: Hyper Refinement Stack, designed to progressively capture coarse-to-fine high-order correlations through a series of hypergraph learning stages, and Anchor-based Hypergraph Neural Network, which employs a two-stage sampling strategy to construct collaborative hypergraphs, ensuring robust modeling of global structures. Extensive experiments on multiple datasets demonstrate that our approach consistently outperforms state-of-the-art 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:33118cbbe01e5c14bf6ab8b93eef511eCVF · CVPR 2026

Generative Adversarial Perturbations with Cross-paradigm Transferability on Localized Crowd Counting

Anisha, Alabi Mehzabin, Wang, Guangjing, Chellappan, Sriram

原文摘要

State-of-the-art crowd counting and localization are primarily modeled using two paradigms: density maps and point regression. Given the field's security ramifications, there is active interest in model robustness against adversarial attacks. Recent studies have demonstrated transferability across density-map-based approaches via adversarial patches, but cross-paradigm attacks (i.e., across both density map-based models and point regression-based models) remain unexplored. We introduce a novel adversarial framework that compromises both density map and point regression architectural paradigms through a comprehensive multi-task loss optimization. For point-regression models, we employ scene-density-specific high-confidence logit suppression; for density-map approaches, we use peak-targeted density map suppression. Both are combined with model-agnostic perceptual constraints to ensure that perturbations are effective and imperceptible to the human eye. Extensive experiments demonstrate the effectiveness of our attack, achieving on average a 7X increase in Mean Absolute Error compared to clean images while maintaining competitive visual quality, and successfully transferring across seven state-of-the-art crowd models with transfer ratios ranging from 0.55 to 1.69. Our approach strikes a balance between attack effectiveness and imperceptibility compared to state-of-the-art transferable attack strategies. The source code is available at https://github.com/simurgh7/CrowdGen

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:3329a3c6a0b09beefdf429baebebdcafCVF · CVPR 2026

Camera Control for Text-to-Image Generation via Learning Viewpoint Tokens

Lu, Xinxuan, Fowlkes, Charless, Berg, Alexander C.

原文摘要

Current text-to-image models struggle to provide precise camera control using natural language alone. In this work, we present a framework for precise camera control with global scene understanding in text-to-image generation by learning parametric camera tokens. We fine-tune image generation models for viewpoint-conditioned text-to-image generation on a curated dataset that combines 3D-rendered images for geometric supervision and photorealistic augmentations for appearance and background diversity. Qualitative and quantitative experiments demonstrate that our method achieves state-of-the-art accuracy while preserving image quality and prompt fidelity. Unlike prior methods that overfit to object-specific appearance correlations, our viewpoint tokens learn factorized geometric representations that transfer to unseen object categories. Our work shows that text-vision latent spaces can be endowed with explicit 3D camera structure, offering a pathway toward geometrically-aware prompts for text-to-image generation. Project page: https://randdl.github.io/viewtoken_control/

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:332c1925cd043dd0dad3ff246261cd51CVF · CVPR 2026

Gated KalmaNet: A Fading Memory Layer through Test-time Ridge Regression

Peng, Liangzu, Chattopadhyay, Aditya, Zancato, Luca, Nunez, Elvis, Xia, Wei, Soatto, Stefano

原文摘要

As efficient alternatives to softmax Attention, linear state space models (SSMs) achieve constant memory and linear compute, but maintain only a lossy, fading summary of the past, often leading to inferior performance in recall oriented settings. We propose Gated KalmaNet (GKA), a layer that reduces this gap by accounting for the full past when predicting the next token, while maintaining SSM-style efficiency. GKA is inspired by the Kalman Filter and solves ridge regression problems online at test time, with constant memory and linear time in the sequence length. An insight is that standard Kalman filter equations are numerically unstable in low-precision environments (e.g., bfloat16) and difficult to parallelize on modern GPUs. We address both challenges via two innovations: (1) an adaptive regularization strategy with input-dependent gating that controls the condition number of the problem, ensuring numerical stability and balancing memory retention; (2) the use of Chebyshev Iteration instead of conventional iterative solvers, which we show to be more stable in low-precision settings. To improve scalability, we implement Chebyshev Iteration in a hardware-aware, chunk-wise manner, along with custom kernels for backpropagating through our adaptive regularization and gating mechanisms. On short-context tasks, GKA shows strong language understanding capabilities and outperforms existing SSMs (e.g., Mamba2, and Gated DeltaNet). On long-context tasks, GKA excels at real-world RAG and LongQA tasks up to 128k tokens with more than 10% relative improvement over baselines. Finally, we show GKA outperforms Mamba when extended for ImageNet classification.

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:332e5ecb0ab3f32b8f2096a95fbfd346CVF · CVPR 2026

SARL-STG: A Spatially Aware Reinforcement Learning Framework for Refining MLLMs in Spatio-Temporal Video Grounding

Gao, Hong, Xu, Xiangkai, Zhong, Bin, Yin, Junjie, Kang, Fangyu, Xu, Yutong, Dong, Xiugang, Gao, Xurui 等 9 位作者

原文摘要

Spatio-Temporal Video Grounding (STVG) requires models to localize objects both spatially and temporally. Despite recent progress, existing methods struggle with complex and fine-grained spatial semantics in language descriptions, leading to error propagation from temporal to spatial grounding stages. We attribute this limitation to the lack of iterative refinement between temporal and spatial predictions. To address these challenges, we propose SARL-STG, the first RL-based framework for STVG. It progressively refines spatio-temporal grounding through multi-stage training with dynamic interaction between temporal and spatial modules. Specifically, SARL-STG contains: (1) a unified architecture that seamlessly integrates a pretrained MLLM for temporal reasoning with an open-vocabulary detector for spatial localization, (2) a hierarchical RL training strategy that progresses from coarse temporal to fine-grained spatio-temporal optimization, and (3) a spatial knowledge-injected reward mechanism that uses spatial grounding quality as discriminative signals for temporal refinement. To facilitate training at scale, we also construct STVG-Wild, a large-scale dataset with diverse spatio-temporal annotations. Experiments demonstrate that our method achieves state-of-the-art performance on multiple benchmarks (HCSTVG, VidSTG, Charades-STA, etc.), significantly reducing error accumulation and enhancing both temporal and spatial grounding accuracy.

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:332feec86821c8c2fd57c3c3471fd0dbCVF · CVPR 2026

Prototype-based Causal Intervention for Multi-Label Image Classification

Li, Yanmin, Mao, Zhilong, Wang, Mao, Liu, Lihua, Wu, Jibing, Bao, Weidong

原文摘要

Modern multi-label image classification models suffer from a critical reliance on spurious correlations, failing to learn the underlying causal mechanisms. Many causality-inspired methods are impractical, demanding box-level supervision that is rarely available in real-world datasets. Others rely on static confounder dictionaries, which are inherently inflexible and fail to capture complex biases or adapt to feature space changes during training. To address this, we present prototype-based causal intervention (ProCI), a novel framework that approximates the backdoor adjustment using only image-level supervision. It models confounders as learnable contextual prototypes engineered to represent class-wise co-occurring bias. These prototypes are learned dynamically within a stable memory and leveraged to construct sample-specific bias vectors for an adaptive feature adjustment, effectively counteracting spurious correlations. Experiments on MS-COCO, Pascal VOC, COCO-Stuff, and the challenging Sewer-ML dataset validate our approach. ProCI achieves competitive performance on standard benchmarks while setting a new state-of-the-art on the highly-confounded Sewer-ML. It outperforms the previous best model by a remarkable +5.44 points on the F2-CIW metric. These results demonstrate the effectiveness of our approach in mitigating complex real-world biases using only image-level supervision.

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:333d69b2d55d9243b7ea3556056def07CVF · CVPR 2026

Mitigating Objectness Bias and Region-to-Text Misalignment for Open-Vocabulary Panoptic Segmentation

Kormushev, Nikolay, Šarić, Josip, Kristan, Matej

原文摘要

Open-vocabulary panoptic segmentation remains hindered by two coupled issues: (i) mask selection bias, where objectness heads trained on closed vocabularies suppress masks of categories not observed in training, and (ii) limited regional understanding in vision-language models such as CLIP, which were optimized for global image classification rather than localized segmentation. We introduce OVRCOAT, a simple, modular framework that tackles both. First, a CLIP-conditioned objectness adjustment (COAT) updates background/foreground probabilities, preserving high-quality masks for out-of-vocabulary objects. Second, an open-vocabulary mask-to-text refinement (OVR) strengthens CLIP's region-level alignment to improve classification of both seen and unseen classes with markedly lower memory cost than prior fine-tuning schemes. The two components combine to jointly improve objectness estimation and mask recognition, yielding consistent panoptic gains. Despite its simplicity, OVRCOAT sets a new state of the art on ADE20K (+5.5% PQ) and delivers clear gains on Mapillary Vistas and Cityscapes (+7.1% and +3% PQ, respectively). The code is available here.

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:334a06fde3a8b4e2d5f70671ac8c8fd6CVF · CVPR 2026

Multi-View Hierarchical Alignment Learning for Spatial Transcriptomics

Zhu, Zhengzhong, Liu, Liangjin, Zhou, Pei, Min, Shiquan, Zhu, Jiangping

原文摘要

Spatial transcriptomics provides both spatial coordinates and gene expression profiles, enabling the study of tissue organization and cellular heterogeneity. Despite recent progress, current spatial clustering methods still face two major limitations. First, representations learned from spatial and expression views often differ due to view-specific noise and incomplete structural information. Without enforcing sample-level cross-view consistency, embeddings from the two views may not correspond to the same biological identity, reducing discriminative capability. Second, existing approaches lack effective semantic-level supervision. Although node embeddings capture local neighborhood patterns, they do not explicitly reflect high-level semantic structures. Prototype-based modeling can provide such semantic abstraction, yet current methods seldom align prototypes with node representations, leading to weak semantic consistency. To overcome these issues, we propose a Multi-View Hierarchical Alignment Learning for Spatial Transcriptomics (MHAL). At the sample level, MHAL introduce positive sample alignment to enforce consistency between spatial and expression embeddings. At the semantic level, MHAL design prototype level contrastive learning, where prototypes act as semantic anchors and guide the formation of coherent cluster structures. Together, these two alignment mechanisms progressively ensure both local consistency and global semantic discrimination. Extensive experimental results demonstrate that the proposed hierarchical contrastive multi-view clustering method achieves competitive performance in spatial domain identification compared to other state-of-the-art 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:334f61029adb75b2ea0a35e8d0af86d9CVF · CVPR 2026

Hist2Style: Histogram-Guided Stylization with Bilateral Grids

Galor, Dekel, Pikielny, Adam, Zhang, Zhoutong, Wang, Ke, Waller, Laura, Chen, Jiawen, Chugunov, Ilya

原文摘要

Photorealistic style transfer aims to match the color and tone of an input image to that of a style target while preserving the content and details of the original scene. Although existing large image models can facilitate these kinds of appearance edits, their high computational demands, potential for hallucinations, and limited user control make them unsuitable for high-resolution, real-time workflows. We introduce Hist2Style, a bilateral-grid formulation for fast, edge-aware stylization that preserves visual fidelity by constraining operations to locally affine transforms in bilateral space. Our model distills a large image editing model into a lightweight network by training on a large supervised corpus generated with language and vision-language models, targeting spatially varying color edits. The network conditions on a histogram-based embedding of the style target to provide an interpretable interface for adjusting the output style by modifying the target color distribution. Overall, Hist2Style maintains content structure by construction, avoids hallucinations, and supports real-time, high-resolution photorealistic stylization with interactive user-controllable color and tone adjustments. Our project page is available at \href https://dgalor.github.io/hist2style/ dgalor.github.io/hist2style/ .

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:335855fac0edc0966ad282663a6f05fbCVF · CVPR 2026

EventGait: Towards Robust Gait Recognition with Event Streams

Xu, Senyan, Chen, Shuai, Shen, Chuanfu, Liu, Kean, Sun, Zhijing, Cao, Chengzhi, Fu, Xueyang

原文摘要

Gait recognition enables non-intrusive, privacy-preserving identification but suffers in uncontrolled environments due to illumination and motion sensitivity in conventional cameras. In this work, we explore gait recognition using event cameras, which offer microsecond temporal resolution and high dynamic range, naturally capturing robust dynamic cues and suppressing static noise. Existing event-based approaches typically aggregate event streams into event images over long time windows, thereby discarding fine-grained motion dynamics critical for gait recognition. Therefore, we propose EventGait, an end-to-end dual-stream framework that separately models motion and shape while preserving the advantages of events. Our dynamic stream leverages a Mixture of Spiking Experts (MoSE) with diverse neuron constants for robust dynamic perception across complex motion and illumination scenes, while the static stream learns dense shape representations via Cross-modal Structural Alignment (CroSA) with large vision foundation models. To address the absence of large-scale event-based gait datasets, we introduce a synthesis pipeline and release two new benchmarks: SUSTech1K-E and CCGR-Mini-E. Extensive experiments have shown that event-based gait recognition not only achieves results comparable to camera-based gait recognition under normal conditions but also significantly outperforms it in low-light scenarios. Our approach sets a new state of the art on both synthesized and real-world event-based gait benchmarks, highlighting the robustness and potential of event-driven gait analysis. The code and datasets are released at https://github.com/QUEAHREN/EventGait.

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:335fc688c2b20c127a8418d94dd4325bCVF · CVPR 2026

Refining Few-Step Text-to-Multiview Diffusion via Reinforcement Learning

Zhang, Ziyi, Shen, Li, Ye, Deheng, Luo, Yong, Zhao, Huangxuan, Liu, Meng, Yu, Wei, Zhang, Lefei

原文摘要

Text-to-multiview (T2MV) diffusion models have shown great promise in generating multiple views of a scene from a single text prompt. While few-step backbones enable real-time T2MV generation, they often compromise key aspects of generation quality, such as per-view fidelity and cross-view consistency. Reinforcement learning (RL) finetuning offers a potential solution, yet existing approaches designed for single-image diffusion do not readily extend to the few-step T2MV setting, as they neglect cross-view coordination and suffer from weak learning signals in few-step regimes. To address this, we propose MVC-ZigAL, a tailored RL finetuning framework for few-step T2MV diffusion models. Specifically, its core insights are: (1) a new MDP formulation that jointly models all generated views and assesses their collective quality via a joint-view reward; (2) a novel advantage learning strategy that exploits the performance gains of a self-refinement sampling scheme over standard sampling, yielding stronger learning signals for effective RL finetuning; and (3) a unified RL framework that extends advantage learning with a Lagrangian dual formulation for multiview-constrained optimization, balancing single-view and joint-view objectives through adaptive primal-dual updates under a self-paced threshold curriculum that harmonizes exploration and constraint enforcement. Collectively, these designs enable robust and balanced RL finetuning for few-step T2MV diffusion models, yielding substantial gains in both per-view fidelity and cross-view consistency. Code is available at https://github.com/ZiyiZhang27/MVC-ZigAL.

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:33600b68750d093177692c45f1d22b83CVF · CVPR 2026

MimiCAT: Mimic with Correspondence-Aware Cascade-Transformer for Category-Free 3D Pose Transfer

Chai, Zenghao, Tang, Chen, Wong, Yongkang, Yang, Xulei, Kankanhalli, Mohan

原文摘要

3D pose transfer aims to transfer the pose-style of a source mesh to a target character while preserving both the target's geometry and the source's pose characteristic. Existing methods are largely restricted to characters with similar structures and fail to generalize to category-free settings (e.g., transferring a humanoid's pose to a quadruped). The key challenge lies in the structural and transformation diversity inherent in distinct character types, which often leads to mismatched regions and poor transfer quality. To address these issues, we first construct a million-scale pose dataset across hundreds of distinct characters. We further propose MimiCAT, a cascade-transformer model designed for category-free 3D pose transfer. Instead of relying on strict one-to-one correspondence mappings, MimiCAT leverages semantic keypoint labels to learn a novel soft correspondence that enables flexible many-to-many matching across characters. The pose transfer is then formulated as a conditional generation process, in which the source transformations are first projected onto the target through soft correspondence matching and subsequently refined using shape-conditioned representations. Extensive qualitative and quantitative experiments demonstrate that MimiCAT generalizes plausible poses across diverse character morphologies, surpassing prior approaches restricted to narrow-category transfer (e.g., humanoid-to-humanoid).

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:3360e9e57ccea5c1a1cbf9cf34b48558CVF · CVPR 2026

EmoStyle: Emotion-Driven Image Stylization

Yang, Jingyuan, Bai, Zihuan, Huang, Hui

原文摘要

Art has long been a profound medium for expressing emotions.While existing image stylization methods effectively transform visual appearance, they often overlook the emotional impact carried by styles.To bridge this gap, we introduce Affective Image Stylization (AIS), a task that applies artistic styles to evoke specific emotions while preserving content.We present EmoStyle, a framework designed to address key challenges in AIS, including the lack of training data and the emotion-style mapping.First, we construct EmoStyleSet, a content-emotion-stylized image triplet dataset derived from ArtEmis to support AIS.We then propose an Emotion-Content Reasoner that adaptively integrates emotional cues with content to learn coherent style queries.Given the discrete nature of artistic styles, we further develop a Style Quantizer that converts continuous style features into emotion-related codebook entries.Extensive qualitative and quantitative evaluations, including user studies, demonstrate that EmoStyle enhances emotional expressiveness while maintaining content consistency.Moreover, the learned emotion-aware style dictionary is adaptable to other generative tasks, highlighting its potential for broader applications.Our work establishes a foundation for emotion-driven image stylization, expanding the creative potential of AI-generated art.

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:3365001d8d05084e790da1881b78b38aCVF · CVPR 2026

SketchVL: Policy Optimization via Fine-Grained Credit Assignment for Chart Understanding and More

Huang, Muye, Zhang, Lingling, Li, Yifei, Wu, Yaqiang, Liu, Jun

原文摘要

Charts are high-density visual carriers of complex data and medium for information extraction and analysis. Due to the need for precise and complex visual reasoning, automated chart understanding poses a significant challenge to existing Multimodal Large Language Models (MLLMs). Many MLLMs trained with reinforcement learning (RL) face the challenge of credit assignment. Their advantage estimation, typically performed at the trajectory level, cannot distinguish between correct and incorrect reasoning steps within a single generated response. To address this limitation, we introduce SketchVL, a novel MLLM that optimized with FinePO, a new RL algorithm designed for fine-grained credit assignment within each trajectory. SketchVL's methodology involves drawing its intermediate reasoning steps as markers on the image and feeding the annotated image back to itself, creating a robust, multi-step reasoning process. During training, the FinePO algorithm leverages a Fine-grained Process Reward Model (FinePRM) to score each drawing action within a trajectory, thereby precisely assigning credit for each step. This mechanism allows FinePO to more strongly reward correct tokens when a trajectory is globally successful, and more heavily penalize incorrect tokens when the trajectory is globally suboptimal, thus achieving fine-grained reinforcement signals. Experiments show that SketchVL learns to align its step-level behavior with the FinePRM, achieving an average performance gain of 7.23% over its base model across chart datasets, natural image datasets, and mathematics, providing a promising new direction for training powerful 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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首次观测 · 记录 #1CVF · CVPR 2026:cvf:336b0d28236418dfe037945e220bcbfaCVF · CVPR 2026

Geometry-Aligned and Anomaly-Aware Reconstruction for 3D Anomaly Detection

Wu, Linchun, Zou, Qin, Yue, Yuanhao, Wang, Zhongyuan

原文摘要

Point cloud anomaly detection is crucial in automated manufacturing, with reconstruction-based diffusion methods emerging as a mainstream solution. However, these approaches still face two major challenges: (1) geometry violation, where random noise perturbations deviate from local surface normals, causing structural distortion; and (2) undistinguished reference regions, where uniformly applied coarse anomaly embeddings during denoising blur normal details and impede accurate anomaly recovery. To address these issues, we propose AARD, a geometry-aligned and anomaly-aware diffusion reconstruction framework. We argue that high-fidelity anomaly detection requires a principled reformulation of the diffusion process: noise should align with geometry to preserve structures, and reconstruction can be better guided by anomaly-aware references to discriminatively recover normal details while correcting defects. AARD progressively aligns noise directions with vertex normals while maintaining vertex-graph consistency, and employs an adaptive transformer that assigns normal references to anomalous regions and input references to normal areas. Experiments on Anomaly-ShapeNet and Real3D-AD show that AARD consistently outperforms state-of-the-art approaches, achieving superior geometric fidelity and robust anomaly localization.

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:336f0a9d9de2d7cf8841f358d383dee9CVF · CVPR 2026

Reclaiming Lost Text Layers for Source-Free Cross-Domain Few-Shot Learning

Zhang, Zhenyu, Chen, Guangyao, Zou, Yixiong, Li, Yuhua, Li, Ruixuan

原文摘要

Source-Free Cross-Domain Few-Shot Learning (SF-CDFSL) focuses on fine-tuning with limited training data from target domains (e.g., medical or satellite images), where CLIP has recently shown promising results due to its generalizability to downstream tasks. Current works indicate CLIP's text encoder is more suitable for cross-domain tasks, however, we find that removing certain middle layers of the text encoder can effectively improve performance in SF-CDFSL, which we call the Lost Layers. In this paper, we delve into this phenomenon for a deeper understanding. We discover that instead of being harmful for the SF-CDFSL task, the information in these layers is actually beneficial, but visual gaps prevent this useful information from being fully utilized, making these layers seem redundant.Based on this understanding, unlike current works that simply remove these layers, we propose a method to teachs the model to re-utilize information in these lost layersat both the layer and encoder levels, guiding the re-learning of the visual branch under domain shifts. Our approach effectively addresses the issue of underutilized information in the text encoder. Extensive experiments across various settings, backbones (CLIP, SigLip, PE-Core), and tasks (4 CDFSL datasets and 10 Meta-dataset datasets) demonstrate the effectiveness of our method. We will release the code.

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:33765f6cbaa7aa356aa57adb249a8753CVF · CVPR 2026

Learning 3D Reconstruction with Priors in Test Time

Zhou, Lei, Wu, Haoyu, Dave, Akshat, Samaras, Dimitris

原文摘要

We introduce a test-time framework for multiview Transformers (MVTs) that incorporates priors (e.g., camera poses, intrinsics, and depth) to improve 3D tasks without retraining or modifying pre-trained image-only networks. Rather than feeding priors into the architecture, we cast them as constraints on the predictions and optimize the network at inference time. The optimization loss consists of a self-supervised objective and prior penalty terms. The self-supervised objective captures the compatibility among multi-view predictions and is implemented using photometric or geometric loss between renderings from other views and each view itself. Any available priors are converted into penalty terms on the corresponding output modalities. Across a series of 3D vision benchmarks, including point map estimation and camera pose estimation, our method consistently improves performance over base MVTs by a large margin. On the ETH3D, 7-Scenes, and NRGBD datasets, our method reduces the point-map distance error by more than half compared with the base image-only models. Our method also outperforms retrained prior-aware feed-forward methods, demonstrating the effectiveness of our test-time constrained optimization (TCO) framework for incorporating priors into 3D vision tasks. The code is available at https://github.com/cvlab-stonybrook/TCO.

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:338a6e3bd52cc859d435ce827807db09CVF · CVPR 2026

BoostSLT: Boosting Sign Language Translation via a Plug-and-Play Diffusion-Based Semantic Enhancer

Han, Changzhou, Ma, Wanlun, Tang, Xi, Hu, Kun, Wen, Sheng, Xiang, Yang

原文摘要

Sign Language Translation (SLT) converts continuous sign videos into spoken language text, yet current models, whether gloss-based or gloss-free, struggle with long or discourse-level inputs. Recent architectures such as TwoStreamNetwork and CV-SLT have nearly saturated short-sentence accuracy, but their performance degrades on long sentences and multi-sentence paragraphs. In real scenarios such as news, interviews or daily conversations, signers naturally produce extended signing sequences with complex contextual dependencies. Moreover, identifying precise gloss boundaries remains a key obstacle, while gloss-based methods, though often superior, incur heavy annotation costs. The community therefore needs a solution that mitigates gloss dependency while preserving translation quality. We present BoostSLT, a context-aware framework for enhancing semantic consistency over long sign sequences without gloss supervision. Instead of requiring explicit gloss segmentation, BoostSLT introduces an Energy-Aware Temporal Segmentation (EAT-Seg) module that dynamically partitions videos into semantically coherent fragments, followed by a Diffusion-based Semantic Reconstruction (DSR) module that stitches and refines fragment-level translations into globally fluent paragraphs. The framework is plug-and-play and model-agnostic, seamlessly integrating with existing gloss-based or gloss-free pipelines across languages. Experiments on PHOENIX-2014T, CSL-Daily, and Auslan-Daily show consistent BLEU and ROUGE-L gains, confirming that diffusion-driven semantic reconstruction effectively bridges local accuracy and global coherence in long-form SLT. Code is available at github.com/K1sna/BoostSLT.

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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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首次观测 · 记录 #1CVF · CVPR 2026:cvf:3397153ac5d164c87aaee784bee26ec7CVF · CVPR 2026

SketchDeco: Training-Free Latent Composition for Precise Sketch Colourisation

Utintu, Chaitat, Song, Yi-Zhe

原文摘要

We introduce SketchDeco, a training-free approach to sketch colourisation that bridges the gap between professional design needs and intuitive, region-based control. Our method empowers artists to use simple masks and colour palettes for precise spatial and chromatic specification, avoiding both the tediousness of manual assignment and the ambiguity of text-based prompts. We reformulate this task as a novel, training-free composition problem. Our core technical contribution is a guided latent-space blending process: we first leverage diffusion inversion to precisely "paint" user-defined colours into specified regions, and then use a custom self-attention mechanism to harmoniously blend these local edits with a globally consistent base image. This ensures both local colour fidelity and global harmony without requiring any model fine-tuning. Our system produces high-quality results in 15--20 inference steps on consumer GPUs, making professional-quality, controllable colourisation accessible.

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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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A Causal Marriage between VLM and IRM from Understanding to Reasoning

Chen, Ziliang, Xiao, Tianang, Zhang, Jusheng, Zheng, Yongsen, Liu, Yang, Lai, Zhao-rong, Lin, Liang

原文摘要

Vision-Language Models (VLMs) like CLIP exhibit extraordinary out-of-distribution (OOD) generalization, while the theoretical foundations underlying this robustness remain largely unexplored. This work establishes a connection between CLIP and Invariant Risk Minimization (IRM), the principled paradigm to overcome OOD problems, through token-level causal representation learning. Our key insight is that CLIP's contrastive objective, when optimally trained, recovers modality-invariant causal factors at the word-and-phrase granularity. By decomposing text prompts into class-specific tokens (causal factors) and class-agnostic context tokens (environmental factors), we prove that a vocabulary-constrained InfoNCE objective becomes formally equivalent to IRM's invariance criterion. Grounded in this equivalence, we propose a mid-training paradigm aiming to inject invariant learning signals into pre-trained CLIP without architectural modification, yielding CLIP-IRM with superior OOD performance. We further extend this causal alignment to multimodal reasoning via using CLIP-IRM's invariant alignment scores as process-level rewards in reinforcement learning, effectively transplanting IRM's guarantees to robust sequential decision-making in Multimodal Large Language Models. Extensive experiments validate our theoretical framework and present substantial improvements in both multimodal OOD understanding and reasoning tasks.

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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Zero-shot Detection of AI-Generated Image via RAW-RGB Alignment

Wu, Haiwei, Li, Fengpeng, Tu, Zhilin, Li, Yuanman, Li, Xiong, Zhou, Jiantao

原文摘要

Advances in generative AI (GenAI) have increasingly complicated the identification of synthetic images, prompting the proposal of numerous zero-/few-shot detection methods to counter unknown GenAI better. However, we observe that existing detectors often misclassify synthetic images with physical transformations (e.g., print+scan) as real. The essence of this observation lies in: should images remapped from the physical world to digital space still be categorized as "Synthetic"? Furthermore, the definition of what constitutes real and synthetic images urgently needs to be clarified. We first boldly propose that the authenticity of an image depends on whether it originates from the physical world, i.e., it is necessary to verify the original correlation between the digital image and the physical world. To this end, we first analyze the physical-to-digital mapping process: illumination signals are captured by camera sensors as RAW data, which is then converted into RGB data via camera internal parameters. This process embodies unique physical cues inherent to real scenes. Based on this, we propose a novel forensic feature termed alignment trace, which is constructed by modeling a shared RAW-RGB feature space. This trace captures the inherent parameter correlations of real images in the physical-to-digital conversion process, thereby indirectly verifying the physical origin of the image. Experiments demonstrate that our method achieves state-of-the-art zero-shot detection using only real RAW-RGB data pairs. When additional prior knowledge is provided, the method can be easily fine-tuned to achieve better cross-domain detection performance. We hope this work provides a new baseline for zero-shot synthetic detection and, more significantly, inspires the forensics community to explore the essential distinctions between real and synthetic images.

cs.CV

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

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ReAttnCLIP: Training-Free Open-Vocabulary Remote Sensing Image Segmentation via Re-defined Attention in CLIP

Niu, Xin, Zhao, Manqi, Jiang, Dongsheng, Wu, Yingying, Su, Bing

原文摘要

Remote sensing image segmentation is critical for a range of applications, including natural disaster monitoring and precision agriculture. Open-vocabulary segmentation enhances flexibility by removing fixed category constraints, enabling more fine-grained and adaptive scene understanding. Unlike CLIP's original pretraining objective, which emphasizes global image-text alignment, segmentation tasks require accurate and discriminative patch-level representations to support precise pixel-wise predictions. As a result, the quality of attention maps--particularly those generated in the final transformer layers--plays a pivotal role in guiding inter-region interactions. However, current methods generate suboptimal representations when capturing the complex spatial hierarchies in remote sensing. We address this gap by optimizing CLIP's 197x197 attention matrix through three key modifications: (1) substituting the 196x196 patch-to-patch submatrix with intermediate-layer feature similarities to preserve spatial structures; (2) prioritizing intermediate-layer attention for global-to-local (class-to-patch) token alignment to reduce classification interference; (3) disabling the \texttt [CLS] token's self-attention to mitigate bias. Extensive experiments on eight remote sensing benchmarks and two building/road extraction datasets demonstrate that our method achieves state-of-the-art performance among existing training-free approaches.

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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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MotionScale: Reconstructing Appearance, Geometry, and Motion of Dynamic Scenes with Scalable 4D Gaussian Splatting

Zhou, Haoran, Lee, Gim Hee

原文摘要

Realistic reconstruction of dynamic 4D scenes from monocular videos is essential for understanding the physical world. Despite recent progress in neural rendering, existing methods often struggle to recover accurate 3D geometry and temporally consistent motion in complex environments. To address these challenges, we propose MotionScale, a 4D Gaussian Splatting framework that scales efficiently to large scenes and extended sequences while maintaining high-fidelity structural and motion coherence. At the core of our approach is a scalable motion field parameterized by cluster-centric basis transformations that adaptively expand to capture diverse and evolving motion patterns. To ensure robust reconstruction over long durations, we introduce a progressive optimization strategy comprising two decoupled propagation stages: 1) A background extension stage that adapts to newly visible regions, refines camera poses, and explicitly models transient shadows; 2) A foreground propagation stage that enforces motion consistency through a specialized three-stage refinement process. Extensive experiments on challenging real-world benchmarks demonstrate that MotionScale significantly outperforms state-of-the-art methods in both reconstruction quality and temporal stability. Project page: https://hrzhou2.github.io/motion-scale-web/.

cs.CV

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

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AToken: A Unified Tokenizer for Vision

Lu, Jiasen, Song, Liangchen, Xu, Mingze, Ahn, Byeongjoo, Wang, Yanjun, Chen, Chen, Dehghan, Afshin, Yang, Yinfei

原文摘要

We present AToken, the first unified visual tokenizer that achieves both high-fidelity reconstruction and semantic understanding across images, videos, and 3D assets. Unlike existing tokenizers that specialize in either reconstruction or understanding for single modalities, AToken encodes these diverse visual inputs into a shared 4D latent space, unifying both tasks and modalities in a single framework. Specifically, we introduce a pure transformer architecture with 4D rotary position embeddings to process visual inputs of arbitrary resolutions and temporal durations. To ensure stable training, we introduce an adversarial-free training objective that combines perceptual and Gram matrix losses, achieving state-of-the-art reconstruction quality. By employing a progressive training curriculum, AToken gradually expands from single images, videos, and 3D, and supports both continuous and discrete latent tokens. AToken achieves 0.21 rFID with 82.2% ImageNet accuracy for images, 3.01 rFVD with 40.2% MSRVTT retrieval for videos, and 28.28 PSNR with 90.9% classification accuracy for 3D.. In downstream applications, AToken enables both visual generation tasks (e.g., image generation with continuous and discrete tokens, text-to-video generation, image-to-3D synthesis) and understanding tasks (e.g., multimodal LLMs), achieving competitive performance across all benchmarks. These results shed light on the next-generation multimodal AI systems built upon unified visual tokenization.

cs.CV

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

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Taming Sampling Perturbations with Variance Expansion Loss for Latent Diffusion Models

Li, Qifan, Zhou, Xingyu, Zhang, Jinhua, You, Weiyi, Gu, Shuhang

原文摘要

Latent diffusion models have emerged as the dominant framework for high-fidelity and efficient image generation, owing to their ability to learn diffusion processes in compact latent spaces. However, while previous research has focused primarily on reconstruction accuracy and semantic alignment of the latent space, we observe that another critical factor, robustness to sampling perturbations, also plays a crucial role in determining generation quality. Through empirical and theoretical analyses, we show that the commonly used b-VAE-based tokenizers in latent diffusion models, tend to produce overly compact latent manifolds that are highly sensitive to stochastic perturbations during diffusion sampling, leading to visual degradation. To address this issue, we propose a simple yet effective solution that constructs a latent space robust to sampling perturbations while maintaining strong reconstruction fidelity. This is achieved by introducing a Variance Expansion loss that counteracts variance collapse and leverages the adversarial interplay between reconstruction and variance expansion to achieve an adaptive balance that preserves reconstruction accuracy while improving robustness to stochastic sampling. Extensive experiments demonstrate that our approach consistently enhances generation quality across different latent diffusion architectures, confirming that robustness in latent space is a key missing ingredient for stable and faithful diffusion sampling. Our project page: https://github.com/CVL-UESTC/VE-Loss .

cs.CV

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

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VideoCoF: Unified Video Editing with Temporal Reasoner

Yang, Xiangpeng, Xie, Ji, Yang, Yiyuan, Ma, Yue, Huang, Yan, Xu, Min, Wu, Qiang

原文摘要

Existing video editing methods face a critical trade-off: expert models offer precision but rely on task-specific priors like masks, hindering unification; conversely, unified temporal in-context learning models are mask-free but lack explicit spatial cues, leading to weak instruction-to-region mapping and imprecise localization. To resolve this conflict, we propose VideoCoF, a novel Chain-of-Frames approach inspired by Chain-of-Thought reasoning. VideoCoF enforces a "seeing, reasoning, then editing" procedure by compelling the video diffusion model to first predict reasoning tokens (edit-region latents) before generating the target video tokens. This explicit reasoning step removes the need for user-provided masks while achieving precise instruction-to-region alignment and fine-grained video editing. Furthermore, we introduce a RoPE alignment strategy that leverages these reasoning tokens to ensure motion alignment and enable length extrapolation beyond the training duration. We demonstrate that with a minimal data cost of only 50k video pairs, VideoCoF achieves state-of-the-art performance on VideoCoF-Bench, validating the efficiency and effectiveness of our approach. Our code, weight, data are available at https://github.com/knightyxp/VideoCoF.

cs.CV

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

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ProgTrack: A Multi-Object Tracking Algorithm with Progressive Matching Strategy

Zhang, Chenhui, Dong, Guoqing, Peng, Weijie

原文摘要

Multi-object tracking (MOT) based on unmanned aerial vehicle (UAV) aims to identify and continuously track the positions of multiple ground targets during UAV flight. Current mainstream methods utilize appearance matching and motion matching to match targets in consecutive frames. However, these methods often fail in the following scenarios: First, scenarios with multi-scale targets, where small targets have weak appearance features and small bounding boxes; second, scenarios with complex backgrounds or occlusions, where the background or occlusions interfere with the appearance features and change the bounding box size of targets; third, scenarios where the UAV lens shakes, rotates, or zooms, leading to misalignment between consecutive frames; and fourth, scenarios with high targets similarity, where the appearance features between targets are difficult to distinguish, such as vehicles on a road. To address these issues, we propose a multi-object tracking algorithm, ProgTrack, based on a multi-stage progressive matching mechanism. This algorithm simulates human eye tracking strategies, employing a progressive process of "first matching easily matched large targets, then matching difficult-to-match small targets, and finally matching the remaining mixed-scale targets." Similarly, ProgTrack employs three strategies for target matching at different scales and appearances: a simple Local Motion Information (LMI) matching strategy for large targets, a complex Context Enhancement Feature (CE-Feature) matching strategy for small targets, and a Global Motion Information (GMI) matching strategy for multi-scale targets matching, thereby achieving target matching. On the VisDrone2019 UAV tracking dataset, ProgTrack achieves MOTA, MOTP, and IDF1 scores of 40.2, 77.5, and 52.8, respectively, demonstrating state-of-the-art performance among ten methods.

cs.CV

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

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Spectral Super-Resolution via Adversarial Unfolding and Data-Driven Spectrum Regularization: From Multispectral Satellite Data to NASA Hyperspectral Image

Young, Si-Sheng, Lin, Chia-Hsiang

原文摘要

The European Space Agency's Sentinel-2 satellite provides global multispectral coverage for remote sensing (RS) applications. However, limited spectral resolution (12 bands) and non-unified spatial resolution (60/20/10 m) restrict their practicality. In contrast, the high spectral-spatial resolution sensor (e.g., NASA's AVIRIS-NG) covers only the American region due to practical considerations. This raises a fundamental question: "Can a global hyperspectral coverage be achieved by reconstructing Sentinel-2 data to NASA hyperspectral images?" This study aims to achieve spectral super-resolution from 12-to-186 and unify the spatial resolution of Sentinel-2 data to 5 m. To enable a reliable and efficient reconstruction, we formulate a novel deep unfolding framework regularized by a data-driven spectrum prior from PriorNet, instead of relying on implicit deep priors as conventional deep unfolding does. Moreover, an adversarial term is integrated into the unfolded architecture, enabling the discriminator to guide the reconstruction in both the training and testing phases; we term this novel concept unfolding adversarial learning (UAL). Experiments show that our UALNet outperforms the next-best Transformer in PSNR, SSIM, and SAM, while requiring only 15% MACs and 20 times fewer parameters. The associated code is publicly available at https://github.com/IHCLab/UALNet.

cs.CV

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

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