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首次观测 · 记录 #1CVF · CVPR 2026:cvf:03bf907539303fad0e549c8c619e1646CVF · CVPR 2026

Translating Signals to Languages for sEMG-Based Activity Recognition

Wang, Ming, Qu, Haoxuan, Ke, Qiuhong, Zhou, Wei, Rahmani, Hossein, Liu, Jun

原文摘要

Surface electromyography (sEMG) signal-based activity recognition has attracted increasing research attention in recent years. To develop accurate sEMG signal-based activity recognizers, numerous approaches have been proposed. Some studies focus on designing larger and more expressive model architectures to enhance the representational capacity of sEMG signals, while others aim to enrich model priors through large-scale pretraining, thereby improving recognition performance. Recently, large language models (LLMs) have shown remarkable generalization and reasoning capabilities in natural language processing, whose implicit knowledge, learned from extensive linguistic descriptions of actions, opens new possibilities for interpreting sEMG signals and inferring activity intentions. Motivated by this, we propose LLM-sEMG, a novel framework that leverages LLMs as sEMG activity recognizers. Within this framework, we design a language-oriented mapping mechanism that converts continuous sEMG sequences into "sEMG language," integrating several strategies to further facilitate the signal-to-language mapping process. Extensive experiments demonstrate that the proposed framework achieves highly accurate sEMG signal-based activity recognition using large language 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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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:0404de2ca951002c19042f280ae6d426CVF · CVPR 2026

UAST: Unified Active Search and Tracking for Arbitrary Targets with UAVs

Qin, Liang, Wang, Min, Lu, Xingyu, Qiu, Aowen, Zhou, Wengang, Li, Houqiang

原文摘要

Active search and tracking of arbitrary targets by Unmanned Aerial Vehicles (UAVs) in cluttered environments remains a highly challenging problem. Existing methods either construct complex modular pipelines, leading to substantial computational costs, or adopt end-to-end controllers that often fail to generalize across different targets and scenes. Moreover, search and tracking are typically treated separately despite their strong interdependence. In this paper, we present UAST, a simple yet effective mapping-free framework that unifies active search and persistent tracking using only RGB-D observations. The proposed system couples a dual-branch perception module with a Rule-Based Point Search Policy that adaptively switches between tracking and search-based recovery. A lightweight control network generates dynamically feasible trajectories directly from fused perception and UAV states. Furthermore, we introduce a training strategy with an elaborated tracking-aware visibility loss and a tailored data construction. Extensive experiments in both simulated and real-world environments show that our approach achieves higher success rates, more stable long-term tracking, and faster target search compared with existing methods, while maintaining high efficiency. The code is available at https://github.com/qinliangql/UAST.

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:1625eb18608cab62f15c6e8ff5eb6459CVF · CVPR 2026

Scalable Multi-View Subspace Clustering with Tensorized Anchor Guidance

Jia, Miao, Hu, Xingchen, Liu, Jiyuan, Wang, Siwei, Wang, Min, Chen, Zijian

原文摘要

Anchor-based multi-view clustering methods have gained significant attention for their effectiveness in handling large-scale datasets in recent years. The performance of these methods is highly dependent on anchor quality. However, current methods neglect the interactive relationships among cross-view anchors, failing to effectively discover and exploit consistent and complementary information, leading to noisy or suboptimal anchor representations. In this paper, we propose a novel scalable multi-view subspace clustering method with tensorized anchor guidance, which directly couples anchors across views to improve clustering performance. Specifically, we construct a third-order anchor tensor from view-specific anchors in a low-dimensional latent space. By imposing a tensor Schatten p-norm constraint on the anchor tensor, we can explicitly capture cross-view low-rank structure and jointly exploit consistent and complementary information among anchors. Moreover, the tensorized anchor regularizer is independent of the number of samples, which reduces both time and space complexity. Experimental results on seven datasets demonstrate that SMVS-TAG achieves superior effectiveness and stability compared to state-of-the-art large-scale MVC methods. Our code is available at https://github.com/Jiamiao2024/SMVS-TAG.

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:7474b3d1241cff69f1872dfb7a29ba12CVF · CVPR 2026

M3DLayout: A Multi-Source Dataset of 3D Indoor Layouts and Structured Descriptions for 3D Generation

Zhang, Yiheng, Cai, Zhuojiang, Wang, Mingdao, Guo, Meitong, Li, Tianxiao, Lin, Li, Wang, Yuwang

原文摘要

In text-driven 3D scene generation, object layout serves as a crucial intermediate representation that bridges high-level language instructions with detailed geometric output. It not only provides a structural blueprint for ensuring physical plausibility but also supports semantic controllability and interactive editing. However, the learning capabilities of current 3D indoor layout generation models are constrained by the limited scale, diversity, and annotation quality of existing datasets. To address this, we introduce M3DLayout, a large-scale, multi-source dataset for 3D indoor layout generation. M3DLayout comprises 21,367 layouts and over 433k object instances, integrating three distinct sources: real-world scans, professional CAD designs, and procedurally generated scenes. Each layout is paired with detailed structured text describing global scene summaries, relational placements of large furniture, and fine-grained arrangements of smaller items. This diverse and richly annotated resource enables models to learn complex spatial and semantic patterns across a wide variety of indoor environments. To assess the potential of M3DLayout, we establish a benchmark using both a text-conditioned diffusion model and a text-conditioned autoregressive model. Experimental results demonstrate that our dataset provides a solid foundation for training layout generation models. Its multi-source composition enhances diversity, notably through the Inf3DLayout subset which provides rich small-object information, enabling the generation of more complex and detailed scenes. We hope that M3DLayout can serve as a valuable resource for advancing research in text-driven 3D scene synthesis. All dataset and code will be made public upon acceptance.

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:ae80ab00eacf65fd65c83590f22d0497CVF · CVPR 2026

RADAR: VQ-VAE Decoder of VAR is a Good Student for Restoring Against Degradation by Acceleration

Wang, Ziyang, Zhang, Yue, Wang, Mingdao, Zhang, Yasen, Song, Teer, Tian, Yu, Li, Xueming

原文摘要

Visual Autoregressive Modeling (VAR) has recently emerged as a powerful paradigm for image generation that surpasses diffusion models in efficiency and quality. However, accelerating attention computation in VAR is still challenging because attention patterns across scales exhibit strong and complex semantic biases that early coarse-scale tokens dominate global structure, while fine-scale tokens mainly refine local details. Existing acceleration methods rely on heuristic token pruning or fixed attention masks, lacking a principled way to balance acceleration and semantic fidelity. In this work, we propose a two-stage acceleration framework for VAR. First, we introduce a semantic-cost-aware masking strategy (SCA-Mask) that quantifies the importance of each attention tile and formulates mask shape design as a cost-constrained optimization problem. This enables adaptive pruning under a given compute budget while preserving essential semantic context. Second, we present Post-Acceleration Adaptation (PAA), a decoder-side fine-tuning scheme that employs internal knowledge distillation to restore image quality from pruned latents. PAA does not require external data and uses a lightweight LoRA-based adaptation, providing a highly efficient alternative to retraining the autoregressive transformer. Comprehensive experiments across multiple VAR tasks demonstrate that our method achieves decent speedup with negligible loss of visual fidelity, yielding a principled and effective pathway toward fast and high-quality visual autoregressive generation.

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:dd07ca7136d7f07b184495c3565afd3aCVF · CVPR 2026

Structural Action Transformer for 3D Dexterous Manipulation

Lei, Xiaohan, Wang, Min, Weng, Bohong, Zhou, Wengang, Li, Houqiang

原文摘要

Achieving human-level dexterity in robots via imitation learning from heterogeneous datasets is hindered by the challenge of cross-embodiment skill transfer, particularly for high-DoF robotic hands. Existing methods, often relying on 2D observations and temporal-centric action representation, struggle to capture 3D spatial relations and fail to handle embodiment heterogeneity. This paper proposes the Structural Action Transformer (SAT), a new 3D dexterous manipulation policy that challenges this paradigm by introducing a structural-centric perspective. We reframe each action chunk not as a temporal sequence, but as a variable-length, unordered sequence of joint-wise trajectories. This structural formulation allows a Transformer to natively handle heterogeneous embodiments, treating the joint count as a variable sequence length. To encode structural priors and resolve ambiguity, we introduce an Embodied Joint Codebook that embeds each joint's functional role and kinematic properties. Our model learns to generate these trajectories from 3D point clouds via a continuous-time flow matching objective. We validate our approach by pre-training on large-scale heterogeneous datasets and fine-tuning on simulation and real-world dexterous manipulation tasks. Our method consistently outperforms all baselines, demonstrating superior sample efficiency and effective cross-embodiment skill transfer. This structural-centric representation offers a new path toward scaling policies for high-DoF, heterogeneous manipulators.

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 ↗