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首次观测 · 记录 #1CVF · CVPR 2026:cvf:7e3cde56560b107b54a9250094fc98d6CVF · CVPR 2026

DriveCombo: Benchmarking Compositional Traffic Rule Reasoning in Autonomous Driving

Ma, Enhui, Zhang, Jiahuan, Zheng, Guantian, Tang, Tao, Li, Shengbo Eben, Lu, Yuhang, Zhou, Xia, Zhang, Xueyang 等 13 位作者

原文摘要

Multimodal Large Language Models (MLLMs) are rapidly becoming the intelligence brain of end-to-end autonomous driving systems. A key challenge is to assess whether MLLMs can truly understand and follow complex real-world traffic rules. However, existing benchmarks mainly focus on single-rule scenarios like traffic sign recognition, neglecting the complexity of multi-rule concurrency and conflicts in real driving. Consequently, models perform well on simple tasks but often fail or violate rules in real world complex situations. To bridge this gap, we propose DriveCombo, a text and vision-based benchmark for compositional traffic rule reasoning. Inspired by human drivers' cognitive development, we propose a systematic Five-Level Cognitive Ladder that evaluates reasoning from single-rule understanding to multi-rule integration and conflict resolution, enabling quantitative assessment across cognitive stages. We further propose a Rule2Scene Agent that maps language-based traffic rules to dynamic driving scenes through rule crafting and scene generation, enabling scene-level traffic rule visual reasoning. Evaluations of 14 mainstream MLLMs reveal a pronounced decline in performance as task difficulty increases, especially in the rule conflict resolution task. After splitting the dataset and fine-tuning on the training set, we further observe substantial improvements in both traffic rule reasoning and downstream planning capabilities. These results highlight the effectiveness of DriveCombo in advancing compliant and intelligent autonomous driving systems.

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:7e4eb5c6e8a04ad9e3489b67933f9d8eCVF · CVPR 2026

EchoPOSE: 6D Pose Estimation of Sparse Echocardiograms for Left-Ventricular 3D Shape Reconstruction

Iijima, Lucas, Luo, Yihao, Sesia, Dario, Kaura, Amit, Mayet, Jamil, Yap, Choon Hwai

原文摘要

3D echocardiography provides superior cardiac quantification to traditional 2D echocardiography, which suffers from geometric idealizations and imaging plane misalignment. However, despite its advantages, clinical adoption of 3D echo remains limited due to logistical and visualization challenges. We propose a novel framework that reconstructs the 3D shape of the left ventricle (LV) throughout the cardiac cycle from sparse 2D echocardiographic views routinely acquired in clinical practice, without the need for external hardware or manual tracking. Our method integrates EchoPOSE, a new deep network that automatically estimates the 6D pose (position and orientation) of LV segmentations, with a graph-harmonic algorithm for 3D shape reconstruction. EchoPOSE employs a transformer-based architecture that combines local image features with global multi-view context, and introduces a geometry-aware loss to ensure spatial consistency across intersecting imaging planes. Trained and evaluated on large-scale synthetic data derived from 3D echocardiography and validated on prospectively acquired clinical echocardiograms, EchoPOSE achieves 3.78 mm and 8.65^ \circ pose errors, yielding 87.5% Dice reconstruction accuracy, 1.44% ejection fraction (EF) error, and 3.03% volume error, surpassing alternative deep learning techniques and classical clinical approaches. Notably, the framework remains robust under suboptimal imaging alignment, suggesting that EchoPOSE can reduce the sonography skills required for transducer positioning and allow minimally trained clinicians to perform echo scans.

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:7e5ca90cb6b8ae3d82e6c2c0eb8e8676CVF · CVPR 2026

Learning from Oblivion: Predicting Knowledge-Overflowed Weights via Retrodiction of Forgetting

Jang, Jinhyeok, Kim, Jaehong, Kim, Jung Uk

原文摘要

Pre-trained weights have become a cornerstone of modern deep learning, enabling efficient knowledge transfer and improving downstream task performance, especially in data-scarce scenarios. However, a fundamental question remains: how can we obtain better pre-trained weights that encapsulate more knowledge beyond the given dataset? In this work, we introduce KNowledge-Overflowed Weights (KNOW) prediction, a novel strategy that leverages structured forgetting and its inversion to synthesize knowledge-enriched weights. Our key insight is that sequential fine-tuning on progressively downsized datasets induces a structured forgetting process, which can be modeled and reversed to recover knowledge as if trained on a larger dataset. We construct a dataset of weight transitions governed by this controlled forgetting and employ meta-learning to model weight prediction effectively. Specifically, our KNowledge-Overflowed Weights Nowcaster (KNOWN) acts as a hyper-model that learns the general evolution of weights and predicts enhanced weights with improved generalization. Extensive experiments across diverse datasets and architectures demonstrate that KNOW prediction consistently outperforms Naive fine-tuning and simple weight prediction, leading to superior downstream performance. Our work provides a new perspective on reinterpreting forgetting dynamics to push the limits of knowledge transfer. The code and pre-trained model are available at https://github.com/jjh6297/KNOW

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:7e6618302b133366e3c70ff36e9c81d1CVF · CVPR 2026

Cross-View Distillation and Adaptive Masking for Incomplete Multi-View Multi-Label Classification

Liu, Yadong, Li, Qiaoqi, Wang, Yueying, Fei, Lunke, Wen, Jie

原文摘要

While existing incomplete multi-view multi-label learning methods have achieved promising performance, few studies have focused on the issue of multi-view imbalance. Existing methods using gradient modulation or alternating optimization strategies alleviate this problem but often oversimplify the interaction between views, resulting in persistently poor performance. In response to the challenge, we propose the Cross-view Distillation and Adaptive Masking (CDAM) framework, a novel approach designed to achieve balanced multi-view optimization for the challenging double incomplete multi-view multi-label learning tasks. First, to overcome the performance bottleneck of views, we design a cross-view distillation module. This module aligns low-quality student representations with high-quality teacher representations, thereby effectively mitigating the multi-view imbalance problem. Second, recognizing that distillation may not rectify all low-quality views, we introduce a subsequent adaptive masking module to perform an explicit quality assessment. This module dynamically identifies and masks out any remaining unreliable representations before multi-view fusion, thus preventing low-quality information from corrupting the fused representation. Extensive comparisons with nine state-of-the-art methods on six datasets validate the effectiveness and stability of our method.

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:7e8ca956265fa0f2d2bf83d75bdc165bCVF · CVPR 2026

Remote Sensing Image Super-Resolution for Imbalanced Textures: A Texture-Aware Diffusion Framework

Zhang, Enzhuo, Zhao, Sijie, Muhtar, Dilxat, Li, Zhenshi, Zhang, Xueliang, Xiao, Pengfeng

原文摘要

Generative diffusion priors have recently achieved state-of-the-art performance in natural image super-resolution, demonstrating a powerful capability to synthesize photorealistic details. However, their direct application to remote sensing image super-resolution (RSISR) reveals significant shortcomings. Unlike natural images, remote sensing images exhibit a unique texture distribution where ground objects are globally stochastic yet locally clustered, leading to highly imbalanced textures. This imbalance severely hinders the model's spatial perception. To address this, we propose TexADiff, a novel framework that begins by estimating a Relative Texture Density Map (RTDM) to represent the texture distribution. TexADiff then leverages this RTDM in three synergistic ways: as an explicit spatial conditioning to guide the diffusion process, as a loss modulation term to prioritize texture-rich regions, and as a dynamic adapter for the sampling schedule. These modifications are designed to endow the model with explicit texture-aware capabilities. Experiments demonstrate that TexADiff achieves superior or competitive quantitative metrics. Furthermore, qualitative results show that our model generates faithful high-frequency details while effectively suppressing texture hallucinations. This improved reconstruction quality also results in significant gains in downstream task performance. The source code of our method can be found at https://github.com/ZezFuture/TexAdiff.

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:7edceaa3c7e21f145b5b8d1d3e7cb62eCVF · CVPR 2026

LATTICE: Democratize High-Fidelity 3D Generation at Scale

Lai, Zeqiang, Zhao, Yunfei, Zhao, Zibo, Liu, Haolin, Lin, Qingxiang, Huang, Jingwei, Guo, Chunchao, Yue, Xiangyu

原文摘要

We present LATTICE, a new framework for high-fidelity 3D asset generation that bridges the quality and scalability gap between 3D and 2D generative models. While 2D image synthesis benefits from fixed spatial grids and well-established transformer architectures, 3D generation remains fundamentally more challenging due to the need to predict both spatial structure and detailed geometric surfaces from scratch. These challenges are exacerbated by the computational complexity of existing 3D representations and the lack of structured and scalable 3D asset encoding schemes. To address this, we propose VoxSet, a semi-structured representation that compresses 3D assets into a compact set of latent vectors anchored to a coarse voxel grid, enabling efficient and position-aware generation. VoxSet retains the simplicity and compression advantages of prior VecSet methods while introducing explicit structure into the latent space, allowing positional embeddings to guide generation and enabling strong token-level test-time scaling. Built upon this representation, LATTICE adopts a two-stage pipeline: first generating a sparse voxelized geometry anchor, then producing detailed geometry using a recitified flow transformer. Our method is simple at its core, but supports arbitrary resolution decoding, low-cost training, and flexible inference schemes, achieving state-of-the-art performance on various aspects, and offering a significant step toward scalable, high-quality 3D asset creation.

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:7ee9773af943f883979578bafa2160edCVF · CVPR 2026

Underground Plant Exploration: Non-Destructive 3D Root Assessment with GPR Based on Point Graph Neural Network

Zhou, Yuwei, Lu, Guoyu

原文摘要

This paper presents an innovative approach for non-destructive 3D modeling of plant root structures, which are essential for nutrient and water uptake. While Ground Penetrating Radar (GPR) has been used for detecting subsurface objects with well-defined shapes, such as pipes, accurately reconstructing complex root structures remains a significant challenge. To address this, we propose a novel framework that leverages GPR signal shape priors for target signal detection and curve parameter regression across multiple B-scans. By integrating these detection and regression results, we obtain precise hyperbolic curves representing root structures. To further assess complete and detailed 3D root systems, we design a root shape modeling network that processes sparse 3D slices using a specialized point graph network and an upsampling module. The method can be extended to many applications, including civil engineering, geology, and environmental monitoring.

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:7eef1cd895d8d69f261b2bc507d27cffCVF · CVPR 2026

SegGBC: Justifiable Coarse-to-Fine Granular-Ball Computing for Enhancing Clustering Image Segmentation

Chong, Qianpeng, Zeng, Wenyi, Shen, Xiuxuan, Li, Jiajie, Yin, Qian, Zheng, Xin

原文摘要

As an emerging multi-granularity clustering paradigm, granular-ball computing (GBC) hierarchically represents samples through granular-balls (GBs) to capture compact, multi-scale features. Nevertheless, its effective application to clustering-based segmentation methods (CSMs) remains challenging due to two key issues: representing intrinsic uncertainties and defining a justifiable, semantics-aware quality criterion. To address them, the first segmentation framework based on GBC (SegGBC) is proposed to alleviate the single-granularity limitation of existing CSMs. Concretely, we leverage intuitionistic fuzzy sets (IFS) to explicitly quantify image uncertainty: membership and non-membership encode evidence, and the IFS hesitation degree models residual ambiguity. In addition, a semantic compactness metric criterion (SCM_GB) is designed to characterize semantic information by considering the "stable region" in conjunction with the overall density of GBs. The proposal of "stable region" ensures robust semantics concurrently with high computational efficiency. Extensive experiments demonstrate that the proposed SegGBC achieves promising performance for segmentation. The proposed segmentation GB representation is a plug-and-play front-end, significantly boosting the performance of CSMs by >+3.25% SA and >+3.92% mIoU on standard image and COCO benchmarks. Code is available at supplementary material.

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:7f030fa63d3b6dbb0914df57e0040bdeCVF · CVPR 2026

MERG3R: A Divide-and-Conquer Approach to Large-Scale Neural Visual Geometry

Cheng, Leo Kaixuan, Shaikh, Abdus, Liang, Ruofan, Wu, Zhijie, Guan, Yushi, Vijaykumar, Nandita

原文摘要

Recent advancements in neural visual geometry, including transformer-based models such as VGGT and Pi3, have achieved impressive accuracy on 3D reconstruction tasks. However, their reliance on full attention makes them fundamentally limited by GPU memory capacity, preventing them from scaling to large, unordered image collections. We introduce MERG3R, a training-free divide-and-conquer framework that enables geometric foundation models to operate far beyond their native memory limits. MERG3R first reorders and partitions unordered images into overlapping, geometrically diverse subsets that can be reconstructed independently. It then merges the resulting local reconstructions through an efficient global alignment and confidence-weighted bundle adjustment procedure, producing a globally consistent 3D model. Our framework is model-agnostic and can be paired with existing neural geometry models. Across large-scale datasets--including 7-Scenes, NRGBD, Tanks & Temples, and Cambridge Landmarks--MERG3R consistently improves reconstruction accuracy, memory efficiency, and scalability, enabling high-quality reconstruction when the dataset exceeds memory capacity limits.

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:7f0fef4f7cecc60a9d939f90df8e5a0bCVF · CVPR 2026

Training-Only Heterogeneous Image-Patch-Text Graph Supervision for Advancing Few-Shot Learning Adapters

Mohammad, Mohammed Rahman Sherif Khan, Behera, Ardhendu, Pradhan, Sandip, Kumar, Swagat, Ahmed, Amr

原文摘要

Recent adapter-based CLIP tuning (e.g., Tip-Adapter) is a strong few-shot learner, achieving efficiency by caching support features for fast prototype matching. However, these methods rely on global uni-modal feature vectors, overlooking fine-grained patch relations and their structural alignment with class text. To bridge this gap without incurring inference costs, we introduce a novel asymmetric training-only framework. Instead of altering the lightweight adapter, we construct a high-capacity auxiliary Heterogeneous Graph Teacher that operates solely during training. This teacher (i) integrates multi-scale visual patches and text prompts into a unified graph, (ii) performs deep cross-modal reasoning via a Modality-aware Graph Transformer (MGT), and (iii) applies discriminative node filtering to extract high-fidelity class features. Crucially, we employ a cache-aware dual-objective strategy to supervise this relational knowledge directly into the Tip-Adapter's key-value cache, effectively upgrading the prototypes while the graph teacher is discarded at test time. Thus, inference remains identical to Tip-Adapter with zero extra latency or memory. Across standard 1-16-shot benchmarks, our method consistently establishes a new state-of-the-art. Ablations confirm that the auxiliary graph supervision, text-guided reasoning, and node filtering are the essential ingredients for robust few-shot adaptation.

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:7f237a1ace1e8a995c08b344dab6b984CVF · CVPR 2026

ReManNet: A Riemannian Manifold Network for Monocular 3D Lane Detection

Hong, Chengzhi, Li, Bijun

原文摘要

Monocular 3D lane detection remains challenging due to depth ambiguity and weak geometric constraints. Mainstream methods rely on depth guidance, BEV projection, and anchor- or curve-based heads with simplified physical assumptions, remapping high-dimensional image features while only weakly encoding road geometry. Lacking an invariant geometric-topological coupling between lanes and the underlying road surface, 2D-to-3D lifting is ill-posed and brittle, often degenerating into concavities, bulges, and twists. To address this, we propose the Road-Manifold Assumption: the road is a smooth 2D manifold in R3, lanes are embedded 1D submanifolds, and sampled lane points are dense observations, thereby coupling metric and topology across surfaces, curves, and point sets. Building on this, we propose ReManNet, which first produces initial lane predictions with an image backbone and detection heads, then encodes geometry as Riemannian Gaussian descriptors on the symmetric positive-definite (SPD) manifold, and fuses these descriptors with visual features through a lightweight gate to maintain coherent 3D reasoning. We also propose the 3D Tunnel Lane IoU (3D-TLIoU) loss, a joint point-curve objective that computes slice-wise overlap of tubular neighborhoods along each lane to improve shape-level alignment. Extensive experiments on standard benchmarks demonstrate that ReManNet achieves state-of-the-art (SOTA) or competitive results. On OpenLane, it improves F1 by +8.2% over the baseline and by +1.8% over the previous best, with scenario-level gains of up to +6.6%. The code will be publicly available at https://github.com/changehome717/ReManNet.

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:7f28e7ab4575faa2307e3e1c4f8c6af8CVF · CVPR 2026

EE-RL: Vision Language Guided Reinforcement Learning with Explorer and Expert model for End-to-End Autonomous Driving

Li, Xiaolong, Yang, Lan, Li, Ruyang, Fang, Shan, Liu, Yang, Zhao, Xiangmo

原文摘要

End-to-end driving frameworks, which directly map raw sensor data to vehicle control commands, have shown remarkable potential. However, their performance often deteriorates in sparse-critical scenarios, where rare but safety-sensitive events occur. To address this problem, we propose Explorer-Expert Reinforcement Learning (EE-RL), a novel end-to-end framework that integrates an RL-based explorer, a fine-tuned vision-language model (VLM)-based expert, and a dual replay buffer. EE-RL adopts a collaborative learning strategy in which an explorer and two experts jointly generate experiences from regular driving scenarios to guide policy learning. As training progresses, one of the VLMs focuses on reasoning about sparse-critical scenarios, thereby enhancing learning efficiency and policy optimization in both scenarios. Additionally, the StateHash algorithm is designed to separately measure RGB image similarity and vehicle kinematic similarity, thereby skipping unnecessary VLM reasoning and enabling denser, more effective expert experience generation. Experiments on the CARLA Leaderboard demonstrate that EE-RL significantly outperforms state-of-the-art (SOTA) baselines, achieving +19.82% and +20.98% improvements in driving and infraction scores on Town03, respectively. The EE-RL further achieves 0% accident probability for red-light running and an average driving score of 80.09 in Town05-06, demonstrating robustness, generalizability, and the capability to handle sparse-critical scenarios.

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:7f2b54d92339aa75d4f2bca1ae898b71CVF · CVPR 2026

Linguistic Priors for Visual Decoupling: Towards Symmetric Vision-Brain Alignment

Liu, Dongjun, Dai, Weichen, Qian, Jingsheng, Liu, Honggang, Yi, Hangjie, Kong, Wanzeng

原文摘要

Brain visual decoding aims to recognize and reconstruct perceptual visual content from brain activity, providing a promising potential for the development of brain-computer interfaces and brain-inspired intelligence. However, this task faces a fundamental challenge of information asymmetry: while natural images contain complex visual scenes with objects and backgrounds, the corresponding brain signals reflect focused attention on central objects while being contaminated by various neural noise. Previous methods that directly align visual and brain representations often overlook this inherent asymmetry, resulting in suboptimal decoding performance. To address this, we propose linguistic-prior-guided visual decoupling method, which introduces object-oriented textual descriptions as semantic guidance to explicitly decouple foreground objects from complex backgrounds in natural images. This design enables the model to automatically focus on task-relevant visual concepts while effectively filtering out irrelevant neural noise in brain signals, achieving a transition from asymmetric vision-brain alignment to semantic symmetric alignment. Extensive experiments on the THINGS-EEG and THINGS-MEG datasets demonstrate that our method achieves new state-of-the-art performance in the zero-shot brain-to-image retrieval task. The source code is available at https://github.com/TKQXX/BVSA.

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:7f2da9827e263e3f9031ad37fca15754CVF · CVPR 2026

Back to Source: Open-Set Continual Test-Time Adaptation via Domain Compensation

Yang, Yingkai, Chen, Chaoqi, Huang, Hui

原文摘要

Test-Time Adaptation (TTA) aims to mitigate distributional shifts between training and test domains during inference time. However, existing TTA methods fall short in the realistic scenario where models face both continually changing domains and the simultaneous emergence of unknown semantic classes --- a challenging setting we term Open-set Continual Test-Time Adaptation (OCTTA). The coupling of domain and semantic shifts often collapses the feature space, severely degrading both classification and out-of-distribution detection. To tackle this, we propose DOmain COmpensation (DOCO), a lightweight and effective framework that robustly performs domain adaptation and OOD detection in a synergistic, closed loop. DOCO first performs dynamic, adaptation-conditioned sample splitting to separate likely ID from OOD samples. Then, using only the ID samples, it learns a domain compensation prompt by aligning feature statistics with the source domain, guided by a structural preservation regularizer that prevents semantic distortion. This learned prompt is then propagated to the OOD samples within the same batch, effectively isolating their semantic novelty for more reliable detection. Extensive experiments on multiple challenging benchmarks demonstrate that DOCO outperforms prior CTTA and OSTTA methods, establishing a new state-of-the-art for the demanding OCTTA setting.

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:7f30dc4588d74bd75dbccc8b3b8078dbCVF · CVPR 2026

Residual Primitive Fitting of 3D Shapes with SuperFrusta

Ganeshan, Aditya, Gadelha, Matheus, Groueix, Thibault, Chen, Zhiqin, Chaudhuri, Siddhartha, Kim, Vladimir, Yifan, Wang, Ritchie, Daniel

原文摘要

We introduce a framework for converting 3D shapes into compact and editable assemblies of analytic primitives, directly addressing the persistent trade-off between reconstruction fidelity and parsimony. Our approach combines two key contributions: a novel primitive, termed SuperFrustum, and an iterative inference algorithm, Residual Primitive Fitting (ResFit). SuperFrustum is a analytical primitive that is simultaneously (1) expressive, being able to express various common solids such as cylinders, spheres, cones & their tapered and bent forms, (2) editable, being compactly parameterized with 8 parameters, and (3) optimizable, with a sign distance field differentiable w.r.t. its parameters almost everywhere. ResFit is an unsupervised procedure that interleaves global shape analysis with local optimization, iteratively fitting primitives to the unexplained residual of a shape to discover a parsimonious yet accurate decompositions for each input shape. On diverse 3D benchmarks, our method achieves state-of-the-art results, improving IoU by over 9 points while using nearly half as many primitives as prior work. The resulting assemblies bridge the gap between dense 3D data and human-controllable design, producing high-fidelity and editable shape programs.

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:7f62a23951e5aa33351118cb15362b81CVF · CVPR 2026

Generating Humanless Environment Walkthroughs from Egocentric Walking Tour Videos

Ham, Yujin, Kim, Junho, Boominathan, Vivek, Balakrishnan, Guha

原文摘要

Egocentric walking tour videos provide a rich source of image data to develop rich and diverse visual models of environments around the world. However, the significant presence of humans in frames of these videos due to crowds and eye-level camera perspectives mitigates their usefulness in environment modeling applications. We focus on addressing this challenge by developing a generative algorithm that can realistically remove (i.e., inpaint) humans and their associated shadow effects from walking tour videos. Key to our approach is the construction of a rich semi-synthetic dataset of video clip pairs to train this generative model. Each pair in the dataset consists of an environment-only background clip, and a composite clip of walking humans with simulated shadows overlaid on the background. We randomly sourced both foreground and background components from real egocentric walking tour videos around the world to maintain visual diversity. We then used this dataset to fine-tune the state-of-the-art Casper video diffusion model for object and effects inpainting, and demonstrate that the resulting model performs far better than Casper both qualitatively and quantitatively at removing humans from walking tour clips with significant human presence and complex backgrounds. Finally, we show that the resulting generated clips can be used to build successful 3D/4D models of urban locations.

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:7f6d2347e3b4d8d7485b97493eecf4fbCVF · CVPR 2026

Deeper Thought, Weaker Aim: Understanding and Mitigating Perceptual Impairment during Reasoning in Multimodal Large Language Models

Peng, Ruiying, Wu, Xueyu, Lei, Jing, Hou, Lu, Ma, Yuanzheng, Li, Xiao-Hui

原文摘要

Multimodal large language models (MLLMs) often suffer from perceptual impairments under extended reasoning modes, particularly in visual question answering (VQA) tasks. We identify attention dispersion as the underlying cause: during multi-step reasoning, model's visual attention becomes scattered and drifts away from question-relevant regions, effectively "losing focus" on the visual input. To better understand this phenomenon, we analyze the attention maps of MLLMs and observe that reasoning prompts significantly reduce attention to regions critical for answering the question. We further find a strong correlation between model's overall attention on image tokens and the spatial dispersiveness of model's attention within the image. Leveraging this insight, we propose a training-free Visual Region-Guided Attention (VRGA) framework that selects visual heads based on an entropy-focus criterion and reweights their attention, effectively guiding the model to focus on question-relevant regions during reasoning. Extensive experiments on vision-language benchmarks demonstrate that our method effectively alleviates perceptual degradation, leading to improvements in visual grounding and reasoning accuracy, while offering interpretable insights into how MLLMs process visual information.

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:7f836b5b10374f0f20a2fbfb89a4e539CVF · CVPR 2026

One Algorithm to Align Them All

Pang, Boyi, Ignatyev, Savva, Ippolitov, Vladimir, Khafizov, Ramil, Melnik, Yurii, Voynov, Oleg, Nakhodnov, Maksim, Alanov, Aibek 等 11 位作者

原文摘要

We suggest a new multi-modal algorithm for joint inference of paired structurally aligned samples with Rectified Flow models. While some existing methods propose a codependent generation process, they do not view the problem of joint generation from a structural alignment perspective. Recent work uses Score Distillation Sampling to generate aligned 3D models, but SDS is known to be time-consuming, prone to mode collapse, and often provides cartoonish results. By contrast, our suggested approach relies on the joint transport of a segment in the sample space, yielding faster computation at inference time. Our approach can be built on top of an arbitrary Rectified Flow model operating on the structured latent space. We show the applicability of our method to the domains of image, video, and 3D shape generation using state-of-the-art baselines and evaluate it against both editing-based and joint inference-based competing approaches. We demonstrate a high degree of structural alignment for the sample pairs obtained with our method and a high visual quality of the samples. Our method improves the state-of-the-art for image and video generation pipelines. For 3D generation, it is able to show comparable quality while working orders of magnitude faster.

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:7f8c99639422b2297b3e459b27256484CVF · CVPR 2026

GeoDiT: A Diffusion-based Vision-Language Model for Geospatial Understanding

Liu, Jiaqi, Fu, Ronghao, Liu, Haoran, Sun, Lang, Wang, Qipeng, Yang, Bo

原文摘要

Autoregressive models are structurally misaligned with the inherently parallel nature of geospatial understanding, forcing a rigid sequential narrative onto scenes and fundamentally hindering the generation of structured and coherent outputs. We challenge this paradigm by reframing geospatial generation as a parallel refinement process, enabling a holistic, coarse-to-fine synthesis that resolves all semantic elements simultaneously. To operationalize this, we introduce GeoDiT, the first diffusion-based vision-language model tailored for the geospatial domain. Extensive experiments demonstrate that GeoDiT establishes a new state-of-the-art on benchmarks requiring structured, object-centric outputs. It achieves significant gains in image captioning, visual grounding, and multi-object detection, precisely the tasks where autoregressive models falter. Our work validates that aligning the generative process with the data's intrinsic structure is key to unlocking superior performance in complex geospatial analysis.

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:7f8e84faf9d627b4be9576291711537cCVF · CVPR 2026

GDFA: Geometry-Driven Federated Unlearning with Directional Task Vector Alignment

Weng, Xiuting, Pu, Ruizhi, Yao, Yuanhang, Yue, Kun, Tang, Zhiwen, Yu, Lixing

原文摘要

Federated Learning (FL) is a decentralized framework that not only enables collaborative training with different clients but also ensures their local data privacy. However, when deletion requests arise under privacy regulations, efficiently removing specific client data contributions from target clients can be challenging. Existing unlearning methods face significant limitations under Non-IID (Non-Independent and Identically Distributed) data distributions when attempting to unlearn specific target clients in FL. Models in sharp optimization regions can suffer catastrophic knowledge loss from minor parameter changes, exacerbating this forgetting due to conflicting parameter updates across clients caused by Non-IID data distributions in FL. Empirically, we observe that conflicting updates under Non-IID settings generate misaligned task vectors that fail to isolate target knowledge. Therefore, we exploit the loss landscape geometry in unlearning specific target clients. We demonstrate that migrating models to flat regions can enhance unlearning robustness in Non-IID FL. Correspondingly, we introduce GDFA, a framework that initially transitions the global model to a flat loss domain. Subsequently, relevant clients generate unlearning task vectors, which GDFA filters to retain only directionally consistent components. This process isolates shared knowledge attributes before precise removal through reverse vector aggregation, maximizing knowledge retention. Extensive experiments demonstrate that GDFA outperforms state-of-the-art methods in unlearning efficacy and efficiency across diverse datasets and architectures, with minimal accuracy loss on retained tasks.

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:7f98217f53e845b1bd1af0fc09bcc9d8CVF · CVPR 2026

ReMoRa: Multimodal Large Language Model based on Refined Motion Representation for Long-Video Understanding

Yashima, Daichi, Kurita, Shuhei, Oda, Yusuke, Sugiura, Komei

原文摘要

While multimodal large language models (MLLMs) have shown remarkable success across a wide range of tasks, long-form video understanding remains a significant challenge.In this study, we focus on video understanding by MLLMs.This task is challenging because processing a full stream of RGB frames is computationally intractable and highly redundant, as self-attention have quadratic complexity with sequence length.In this paper, we propose ReMoRa, a video MLLM that processes videos by operating directly on their compressed representations.A sparse set of RGB keyframes is retained for appearance, while temporal dynamics are encoded as a motion representation, removing the need for sequential RGB frames.These motion representations act as a compact proxy for optical flow, capturing temporal dynamics without full frame decoding.To refine the noise and low fidelity of block-based motions, we introduce a module to denoise and generate a fine-grained motion representation.Furthermore, our model compresses these features in a way that scales linearly with sequence length.We demonstrate the effectiveness of ReMoRa through extensive experiments across a comprehensive suite of long-video understanding benchmarks.ReMoRa outperformed baseline methods on multiple challenging benchmarks, including LongVideoBench, NExT-QA, and MLVU. Our project page is available at https://remora-v1rcm.kinsta.page/.

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:7f9d8aaf12b124493dfedd11352f496fCVF · CVPR 2026

Failure Modes for Deep Learning-Based Online Mapping: How to Measure and Address Them

Hubbertz, Michael, Han, Qi, Meisen, Tobias

原文摘要

Deep learning-based online mapping has emerged as a cornerstone of autonomous driving, yet these models frequently fail to generalize beyond familiar environments. We propose a framework to identify and measure the underlying failure modes by disentangling two effects: Memorization of input features and overfitting to known map topologies. We propose metrics based on evaluation subsets that control for geographical proximity and topological similarity between training and validation scenes. We introduce Frechet distance-based reconstruction statistics that capture per-element shape fidelity without threshold tuning, and define complementary failure-mode scores: an input-feature overfitting score quantifying the performance drop when geographic cues disappear, and a topology overfitting score measuring degradation as scenes become topologically novel. Beyond models, we analyze dataset biases and contribute topology-aware diagnostics: A minimum-spanning-tree (MST) diversity metric for training sets and a symmetric coverage metric to quantify topological similarity between splits. Leveraging these, we formulate an MST-based sparsification strategy that reduces redundancy and improves balancing and performance while shrinking training size. Experiments on nuScenes and Argoverse 2 across multiple state-of-the-art models yield more trustworthy assessment of generalization and show that topology-diverse and balanced training sets lead to improved performance. Our results motivate failure-mode-aware protocols and topology-centric dataset design for deployable online mapping.

cs.CV

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

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Feed-forward Gaussian Registration for Head Avatar Creation and Editing

Prinzler, Malte, Gotardo, Paulo, Tang, Siyu, Bolkart, Timo

原文摘要

We present MATCH (Multi-view Avatars from Topologically Corresponding Heads), a multi-view Gaussian registration method for high-quality head avatar creation and editing. State-of-the-art multi-view head avatars require time-consuming head tracking, which is followed by an expensive avatar optimization, often resulting in a total creation time that exceeds one day. MATCH instead directly predicts Gaussian splat textures in correspondence from calibrated multi-view images in 0.5 seconds per frame. While the learned intra-subject correspondence across frames allows us to quickly build personalized head avatars, correspondence across subjects enables various applications such as expression transfer, optimization-free tracking, semantic editing, and identity interpolation. We learn to establish such correspondences end-to-end, with a transformer-based model that predicts textures of Gaussian splats in the fixed UV layout of a template mesh. To this end, we introduce a novel registration-guided attention block, in which each UV map token attends exclusively to image tokens depicting its corresponding mesh region. MATCH outperforms existing methods for novel-view synthesis, geometry registration, and head avatar generation, the latter being 10xfaster than the qualitatively closest baseline. Code and model weights are available under https://malteprinzler.github.io/projects/match

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:80022b7f6e9e289b3bcc29f6b184ea3fCVF · CVPR 2026

Sampling-Aware Quantization for Diffusion Models

Zeng, Qian, Song, Jie, Wan, Yuanyu, Wang, Huiqiong, Song, Mingli

原文摘要

Diffusion models have recently emerged as the dominant approach in visual generation tasks. However, the lengthy denoising chains and the computationally intensive noise estimation networks hinder their applicability in low-latency and resource-limited environments. Previous research has endeavored to address these limitations in a decoupled manner, utilizing either advanced samplers or efficient model quantization techniques. In this study, we uncover that quantization-induced noise disrupts directional estimation at each sampling step, further distorting the precise directional estimations of higher-order samplers when solving the sampling equations through discretized numerical methods, thereby altering the optimal sampling trajectory. To attain dual acceleration with high fidelity, we propose a sampling-aware quantization strategy, wherein a Mixed-Order Trajectory Alignment technique is devised to impose a more stringent constraint on the error bounds at each sampling step, facilitating a more linear probability flow. Extensive experiments on sparse-step fast sampling across multiple datasets demonstrate that our approach preserves the rapid convergence characteristics of high-speed samplers while maintaining superior generation quality. Code is publicly available at: https://github.com/TaylorJocelyn/Sampling-aware-Quantization.

cs.CV

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

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Continuous Exposure-Time Modeling for Realistic Atmospheric Turbulence Synthesis

Zeng, Junwei, Liang, Dong, Huang, Sheng-Jun, Zhan, Kun, Chen, Songcan

原文摘要

Atmospheric turbulence significantly degrades long-range imaging by introducing geometric warping and exposure-time-dependent blur, which adversely affects both visual quality and the performance of high-level vision tasks. Existing methods for synthesizing turbulence effects often oversimplify the relationship between blur and exposure-time, typically assuming fixed or binary exposure settings. This leads to unrealistic synthetic data and limited generalization capability of trained models. To address this gap, we revisit the modulation transfer function (MTF) formulation and propose a novel Exposure-Time-dependent MTF (ET-MTF) that models blur as a continuous function of exposure-time. For blur synthesis, we derive a tilt-invariant point spread function (PSF) from the ET-MTF, which, when integrated with a spatially varying blur-width field, provides a comprehensive and physically accurate characterization of turbulence-induced blur. Building on this synthesis pipeline, we construct ET-Turb, a large-scale synthetic turbulence dataset that explicitly incorporates continuous exposure-time modeling across diverse optical and atmospheric conditions. The dataset comprises 5,083 videos (2,005,835 frames), partitioned into 3,988 training and 1,095 test videos. Extensive experiments demonstrate that models trained on ET-Turb produce more realistic restorations and achieve superior generalization on real-world turbulence data compared to those trained on other datasets. The dataset is publicly available at: github.com/Jun-Wei-Zeng/ET-Turb.

cs.CV

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

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BUSSARD: Normalizing Flows for Bijective Universal Scene-Specific Anomalous Relationship Detection

Schween, Melissa, Kruse, Mathis, Rosenhahn, Bodo

原文摘要

We propose Bijective Universal Scene-Specific Anomalous Relationship Detection (BUSSARD), a normalizing flow-based model for detecting anomalous relations in scene graphs, generated from images. Our work follows a multimodal approach, embedding object and relationship tokens from scene graphs with a language model to leverage semantic knowledge from the real world. A normalizing flow model is used to learn bijective transformations that map object-relation-object triplets from scene graphs to a simple base distribution (typically Gaussian), allowing anomaly detection through likelihood estimation. We evaluate our approach on the SARD dataset containing office and dining room scenes. Our method achieves around 10% better AUROC results compared to the current state-of-the-art model, while simultaneously being five times faster. Through ablation studies, we demonstrate superior robustness and universality, particularly regarding the use of synonyms, with our model maintaining stable performance while the baseline shows 17.5% deviation. This work demonstrates the strong potential of learning-based methods for relationship anomaly detection in scene graphs. Our code is available at https://github.com/mschween/BUSSARD.

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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Adaptive Confidence Regularization for Multimodal Failure Detection

Liu, Moru, Dong, Hao, Fink, Olga, Trapp, Mario

原文摘要

The deployment of multimodal models in high-stakes domains, such as self-driving vehicles and medical diagnostics, demands not only strong predictive performance but also reliable mechanisms for detecting failures. In this work, we address the largely unexplored problem of failure detection in multimodal contexts. We propose Adaptive Confidence Regularization (ACR), a novel framework specifically designed to detect multimodal failures. Our approach is driven by a key observation: in most failure cases, the confidence of the multimodal prediction is significantly lower than that of at least one unimodal branch, a phenomenon we term confidence degradation. To mitigate this, we introduce an Adaptive Confidence Loss that penalizes such degradations during training. In addition, we propose Multimodal Feature Swapping, a novel outlier synthesis technique that generates challenging, failure-aware training examples. By training with these synthetic failures, ACR learns to more effectively recognize and reject uncertain predictions, thereby improving overall reliability. Extensive experiments across four datasets, three modalities, and multiple evaluation settings demonstrate that ACR achieves consistent and robust gains. The source code will be available at https://github.com/mona4399/ACR.

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:80332e511d0b63e3fa7f696d7e687e12CVF · CVPR 2026

HFR and HDR Video from Multi-Attenuated Spikes Using a Rapidly Rotating SpokeND Filter

Chang, Yakun, Huang, Zhaojun, Yang, Siqi, Xiaokaiti, Yeliduosi, Wei, Shikui, Zhao, Yao, Huang, Tiejun, Shi, Boxin

原文摘要

Capturing scenes with both high dynamic range (HDR) and high-speed motion remains challenging for conventional cameras. Existing alternating-exposure approaches exacerbate temporal resolution loss, making them unsuitable for high-speed scenes. Consequently, current solutions typically compromise either spatial resolution through fixed spatial-varying attenuation levels or employ multi-sensor configurations to maintain temporal resolution. In this paper, we leverage an ultra-high speed spike camera to enable spatial and temporal attenuation of incident light, thereby reconstructing high frame rate (HFR) and HDR video with a single sensor. We achieve this by placing a rapidly rotating spoke-pattern neutral density (SpokeND) filter in front of the spike camera, enabling each pixel to periodically capture multi-attenuated spikes while maintaining full spatial resolution. Building on these multi-attenuated spikes, we propose ReST-Net, which comprises the ReGain and ReFine modules. The ReGain module reconstructs spatially consistent frames by learning to recover relative gain from the multi-attenuated spikes, and the ReFine module removes temporal fluctuations to produce temporally consistent HDR videos. Extensive experiments on synthetic and real-world data demonstrate that our method can reconstruct HDR video at up to 2000 FPS.

cs.CV

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

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BrepVGAE: Variational Graph Autoencoder with Unified Latent Representation for B-rep

Guo, Hao, Deng, Liyuan, Dai, Yongkang, Wang, Ruohan, Li, Jiahao, Bai, Yunpeng, Shi, Yilei

原文摘要

Due to the heterogeneity of faces and edges in B-rep, conventional graph-based representations is incapable of establishing a unified formulation for faces and edges, thereby constraining the capabilities of B-rep generative models. We propose a B-rep Variational Graph Auto Encoding (BrepVGAE), the first variational graph autoencoder framework capable of holistically encoding and decoding boundary representations of B-rep models.Firstly, we novelly represent both geometry faces and edges as nodes in a graph representation. We then design a sparse graph autoencoder to aggregate the complete B-rep structure into a compact global latent vector. We then construct a decoder that employs set-based generation, which uses bilinear layers to reconstruct adjacency relationships, i.e., topology, with a single latent vector. Afterwards, the same decoder generates node features for all faces and edges through learnable query vectors and cross-attention mechanisms. Finally, a two-stage training strategy ensures effective coupling of geometry and topology throughout. Comprehensive experiments demonstrate that BrepVGAE significantly outperforms existing methods in reconstruction accuracy, topological validity, and generative diversity. This validates the feasibility and efficacy of decoding complete CAD geometric-topological distributions from a unified latent representation, while also offering novel insights for CAD part retrieval and feature recognition domains.

cs.CV

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

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MARSS: Radar Semantic Segmentation via Modular Attention and State Space Models

Chen, Fengyu, Tan, Tiao, Li, Teng, Quan, Yuantian, Liao, Qingmin

原文摘要

Radar semantic segmentation (RSS) is critical for robust perception in adverse conditions, but poses unique challenges: radar frequency maps are highly anisotropic, multi-scale, sparse and noisy. Conventional CNN or Transformer architectures, designed for camera images, fail to account for these characteristics, leading degraded performance. We propose MARSS (Modular Attention-enhanced Radar Semantic Segmentation), a novel framework that integrates three specialized modules to address radar-specific issues. In the encoder, the RADE module employs lightweight channel self-attention and depthwise convolutions to robustly encode noisy, anisotropic features. In intermediate layers, the RFAF module performs multi-scale feature fusion and region-level attention to isolate salient radar features. The decoder's RADM module combines state space models with axial self-attention to reconstruct segmentation masks with anisotropy and temporality-aware context. These components collectively suppress noise, disentangle range-Doppler features, and enforce spatial-temporal consistency. On the CARRADA dataset, MARSS achieves substantially higher performance than prior RSS methods, especially for small fast-moving targets.

cs.CV

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

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