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首次观测 · 记录 #1CVF · CVPR 2026:cvf:2911f97903fba55ff44918f934b6d78fCVF · CVPR 2026

Functional Mean Flow in Hilbert Space

Li, Zhiqi, Sun, Yuchen, Turk, Greg, Zhu, Bo

原文摘要

We present Functional Mean Flow (FMF) as a one-step generative model defined in infinite-dimensional Hilbert space. FMF extends the one-step Mean Flow framework to functional domains by providing a theoretical formulation for Functional Flow Matching and a practical implementation for efficient training and sampling. We also introduce an x_1-prediction variant that improves stability over the original u-prediction form. The resulting framework is a practical one-step Flow Matching method applicable to a wide range of functional data generation tasks such as time series, images, PDEs, and 3D geometry.

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:2a75940ac91223a728c6b98cc6affafcCVF · CVPR 2026

Do You Have Freestyle? Expressive Humanoid Locomotion via Audio Control

Li, Zhe, Chi, Cheng, Wei, Yangyang, Zhu, Boan, Huang, Tao, Sun, Zhenguo, Peng, Yibo, Wang, Pengwei 等 12 位作者

原文摘要

Humans intuitively move to sound, but current humanoid robots lack expressive improvisational capabilities, confined to predefined motions or sparse commands. Generating motion from audio and then retargeting it to robots relies on explicit motion reconstruction, leading to cascaded errors, high latency, and disjointed acoustic-actuation mapping. We propose RoboPerform, the first unified audio-to-locomotion framework that can directly generate music-driven dance and speech-driven co-speech gestures from audio. Guided by the core principle of "motion = content + style", the framework treats audio as implicit style signals and eliminates the need for explicit motion reconstruction. RoboPerform integrates a ResMoE teacher policy for adapting to diverse motion patterns and a diffusion-based student policy for audio style injection. This retargeting-free design ensures low latency and high fidelity. Experimental validation shows that RoboPerform achieves promising results in physical plausibility and audio alignment, successfully transforming robots into responsive freestyle performers capable of reacting to audio.

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:3250d7397f6040399ee9a50112b66a62CVF · CVPR 2026

Reallocating Attention Across Layers to Reduce Multimodal Hallucination

Lu, Haolang, Chu, Bolun, Fu, WeiYe, Nan, Guoshun, Liu, Junning, Pan, Minghui, Li, Qiankun, Yu, Yi 等 10 位作者

原文摘要

Multimodal large reasoning models (MLRMs) often suffer from hallucinations that stem not only from insufficient visual grounding but also from imbalanced allocation between perception and reasoning processes. Building upon recent interpretability findings suggesting a staged division of attention across layers, we analyze how this functional misalignment leads to two complementary failure modes: perceptual bias in shallow layers and reasoning drift in deeper layers. To alleviate these issues, we propose Functional Head Identification and Class-Conditioned Rescaling, a lightweight, training-free plugin that identifies perception- and reasoning-oriented heads and adaptively rebalances their layerwise contributions. Our method improves reasoning consistency and visual faithfulness without retraining or any architectural modification. Evaluations across three representative MLRMs and five multimodal reasoning benchmarks show an average 4.2 percentage-point gain, with less than 1% additional computation and only 9% baseline latency. Beyond empirical improvements, our study provides an interpretable perspective on regulating cross-layer functional dynamics to enhance the reliability of multimodal reasoning.

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:346206d26b16f716ea213e843f006ae6CVF · CVPR 2026

Hyperbolic Defect Feature Synthesis for Few-Shot Defect Classification

Li, Huimin, Hu, Boxuan, Zhang, Yulin, Zhou, Xiuzhuang, Hu, Junlin

原文摘要

Defect synthesis, as a core technology for addressing the problem of few-shot defect classification, has been widely adopted in industrial scenarios. It helps alleviate the problem of insufficient model generalization capability owing to data scarcity by establishing a data augmentation pipeline. Recently, remarkable progress has been achieved in both explicit defect image generation and implicit defect feature synthesis approaches. However, existing methods are always conducted in Euclidean space. Constrained by the flatness of Euclidean space, it is difficult to synthesize defect data containing complex structures. In this paper, we attempt to explore defect generation in hyperbolic space and propose a hyperbolic defect feature synthesis (HypDFS) method. By modeling the potential defect distribution via a small number of hyperbolic defect prototypes and further optimizing the synthetic defect features with the hierarchical defect contrastive loss in hyperbolic space, our HypDFS method can obtain a better generalized defect representation that is more conducive to downstream few-shot defect classification task. Extensive experiments on the MVTec-FS benchmark and standard MTD dataset under the few-shot settings demonstrate that the proposed HypDFS surpasses the Euclidean baseline by a large margin, showing promising prospects for defect synthesis in hyperbolic space.

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:46c898c875c19ea98c0ad7b2af990c56CVF · CVPR 2026

Self-guided Semantic Inspection for Zero-Shot Composed Image Retrieval

Zhang, Jingjing, Zhang, Lei, Fu, Zheren, Hu, Bo, Mao, Zhendong

原文摘要

Zero-Shot Composed Image Retrieval (ZS-CIR) aims to retrieve target images using a composed query of a reference image and a textual modification, without relying on triplet-based supervision. As the two inputs describe related but semantically unaligned information, the key challenge lies in interpreting their cross-modal discrepancy to infer the user's intended semantic modification. Existing ZS-CIR methods mainly adopt a consistency-driven paradigm, training on semantically aligned image-text pairs with alignment or reconstruction objectives. This paradigm enforces cross-modal agreement but overlooks the semantic discrepancies between modalities that naturally arise during inference. To address this issue, we propose DiffComp (Differentiate-then-Compose), a difference-driven self-supervised framework that actively induces and exploits cross-modal discrepancies during training. It stimulates the model to perceive and reconcile semantic differences across visual and textual modalities, thereby improving consistency between training and inference. The framework consists of three components: Contextual Semantic Super-patches that provide localized and coherent visual representations for downstream perception and composition; Phrase-guided Masking that selectively removes text-aligned visual cues to induce controlled cross-modal discrepancies; and Difference-aware Composition that adaptively integrates visual and textual features according to their degree of semantic difference. Extensive experiments on four ZS-CIR benchmarks show that DiffComp achieves state-of-the-art performance and strong generalization. Our code will be released at https://github.com/Orange1999/DiffComp.

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:833d8569a54d10fdcecfd30adf6f41d8CVF · CVPR 2026

Expanding mmWave Datasets for Human Pose Estimation with Unlabeled Data and LiDAR Datasets

Peng, Zhuoxuan, Zhu, Boan, Zhang, Xingjian, Li, Wenying, Chan, S.-H. Gary

原文摘要

Current millimeter-wave (mmWave) datasets for human pose estimation (HPE) are scarce and lack diversity in both point cloud (PC) attributes and human poses, hindering the generalization ability of their trained models. On the other hand, unlabeled mmWave HPE data and diverse LiDAR HPE datasets are readily available. We propose EMDUL, a novel approach to expand the volume and diversity of an existing mmWave dataset using unlabeled mmWave data and LiDAR datasets. EMDUL consists of two independent modules, namely a pseudo-label estimator to annotate unlabeled mmWave data, and a closed-form converter that translates an annotated LiDAR PC to its mmWave counterpart. Expanding the original dataset with both LiDAR-converted and pseudo-labeled mmWave PCs significantly boosts the performance and generalization ability of all the examined HPE models, reducing 15.1% and 18.9% error for in-domain and out-of-domain settings, respectively. Code is available at https://github.com/Shimmer93/EMDUL.

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:c4a41c776c99f0341c5ebfd68c4b80e8CVF · CVPR 2026

Minerva-Ego: Spatiotemporal Hints for Egocentric Video Understanding

Nagrani, Arsha, Uijlings, Jasper, Buch, Shyamal, Weyand, Tobias, Vijayanarasimhan, Sudheendra, Hu, Bo, Mehran, Ramin, A Ross, David 等 9 位作者

原文摘要

Video reasoning models are a core component of egocentric and embodied agents. However, standard benchmarks for assessing models provide only evaluation of the output (e.g. the answer to a question), without evaluation of inter- mediate reasoning steps, and most provide answers only in the text domain. We introduce Minerva-Ego, a bench- mark for evaluating complex egocentric visual reasoning. We extend recent high-quality video data sources recorded from egocentric / embodied settings with a set of challenging, multi-step multimodal questions and spatiotemporally-dense human-annotated reasoning traces. Benchmarking experiments show that state-of-the-art models still have a large gap to human performance. To investigate this gap in detail, we annotate each reasoning trace in the dataset with the objects of interest required to solve the question, as spatio-temporal mask annotations. Through extensive evaluations, we identify that prompting frontier models with hints of 'where' and when to look yields substantial improvements in performance.

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 ↗