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版本更新 · v3arXiv:2511.18127

Predictive Representation Learning for Manipulation via Streaming Egocentric 3D Hand Forecasting

Ruicong Liu, Yifei Huang, Liangyang Ouyang, Caixin Kang, Yoichi Sato

原文摘要

Learning embodied manipulation from egocentric human data offers a scalable path, reducing the cost of collecting robot-specific demonstrations. However, existing representation learning methods largely stop at perceptual understanding, such as extracting what a scene contains or what an action is. A capable embodied agent is more than understanding: it predicts the next motion. We propose that forecasting 3D human hand motion is one suitable pretraining task, as the representation jointly learns how to manipulate objects and how the motion will unfold next. Building on this hypothesis, we introduce SFHand, the first representation learning method via streaming egocentric 3D hand forecasting. SFHand learns by autoregressively predicting future 3D hand states, including hand type, 2D bounding box, 3D pose, and trajectory, from a continuous stream of video and language instructions. Its autoregressive architecture is equipped with an ROI-enhanced memory layer, capturing temporal context while focusing on salient hand-centric regions. To enable this research, we also introduce SFHand, the first open-source large-scale dataset pairing synchronized 3D hand poses with fine-grained language instructions (247K clips, 3.95M frames). Transferred to downstream embodied control, the learned representation shows state-of-the-art performance on both simulation and real-world manipulation benchmarks, improving success rate by up to 13.4%. Our method also achieves leading 3D hand forecasting accuracy, with 35.8% lower ADE than baseline methods. These results suggest that forecasting hand motion is a better pretraining task for representation learning in embodied manipulation. Projec page: https://ruicongliu.github.io/sfhand/.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07765

BiGrounder: Bidirectional Training Unlocks the Potential of GUI Grounding

Dengyao Shi, Tianyu Fu, Fei Hu, Anyang Su, Hanning Wang, Shuo Wang, Minghui Wu, Chenxu Zhao 等 9 位作者

原文摘要

Graphical User Interface (GUI) grounding aims to map natural language instructions to precise operational coordinates, serving as a critical task for autonomous agent interaction. Current approaches formalize GUI grounding as predicting action-coordinate pairs from instructions, with end-to-end training supervised solely by final coordinates. Training datasets typically provide only a small number of descriptions per target coordinate, whereas real-world scenarios involve a much larger set of varied descriptions for the same target, which limits robustness to linguistic variation. While recent GUI grounding paradigms have begun employing reinforcement learning to enhance localization accuracy, models with limited exploratory capability before reinforcement yield constrained improvements even with identical training data during the reinforcement phase. To overcome these limitations, we propose a bidirectional reward paradigm that enables model self-reinforcement. During SFT, bidirectional training equips the model to generate action descriptions from target coordinates. During RL, the model samples semantically consistent instruction variations on-policy and grounds each variation back to its target. A cycle-consistency gate couples semantic quality to grounding success, turning the model's own instruction policy into a source of continually refreshed reinforcement trajectories. Our 4B-parameter model achieves competitive accuracy across five grounding benchmarks in our evaluation, indicating the effectiveness of the proposed training paradigm. https://github.com/Spell-shi/BiGrounder

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.16482

Cue-guided dual-path reconstruction with adaptive multi-scale response refinement for low-light image enhancement

Yicui Shi, Yuhan Chen, Xiangfei Huang, Zhenguo Wang, Wenxuan Yu, Ying Fang, Jie Li, Guofa Li

原文摘要

Low-light image enhancement (LLIE) supports optical sensing and scene observation when limited photon capture and sensor noise degrade image quality. However, existing methods can introduce colour deviations or blur local details in the enhanced images. To address this challenge, we propose CueDR-Net, a dual-path network that combines brightness adjustment with local content reconstruction through bidirectional cue interaction and adaptive multi-scale refinement. Specifically, a fixed extractor derives luminance variation, colour, and texture cue maps from the input image. These maps are encoded in a cue stream that exchanges features with the image stream through bidirectional attention. The exchanged features support two paths for brightness adjustment and local content reconstruction. The brightness adjustment path predicts pixel wise adjustment maps that control iterative intensity changes in the input image. In parallel, the residual path uses the exchanged features to predict an additive correction for local content reconstruction. To capture weak structures across scales, Adaptive Multi-Scale Response Refinement Convolution (AMRRConv) refines the features used for both predictions. It fuses learnable local transition responses with multi-scale features from factorized three-plane filtering using pixel wise weights. The adjusted image is added to the correction before being mapped to the enhanced image. Evaluations on LOLv1, LSRW-HUAWEI, and LSRW-NIKON demonstrate competitive enhancement performance, supported by ablation studies. AMRRConv also achieves competitive low-light detection performance with a small neck parameter count when integrated into YOLOv11.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.07015

Understand Before Detect: Vision--Language Learning for Omni-Domain Infrared Small Target Detection

Haoyang Yuan, Boyang Li, Yingqian Wang, Yimian Dai, Nuo Chen, Xinfei Huang, Shuqi Yi, Zaiping Lin 等 10 位作者

原文摘要

Omni-domain infrared small target (IRST) detection is crucial for infrared surveillance, yet remains challenging due to heterogeneous imaging domains and inconsistent target characteristics. Previous deep learning-based methods have been developed for visual-only paradigms and achieved promising performance on domain-specific tasks. However, existing methods follow the task-specific supervised learning paradigm. This paradigm simplifies the full-scene infrared observations to sparse target supervision, discarding the semantics that remain invariant across heterogeneous domains. Consequently, detection performance suffers substantially under domain shifts. To handle this issue, we introduce \textbf{``understand before detect''}, a paradigm that formulates omni-domain IRST detection as an understanding-driven process, where holistic infrared target understanding precedes precise detection. Building on this paradigm, we propose \textbf{JinSight}, which first develops holistic IRST understanding through language supervision and then transfers the learned cross-domain representations to precise small-target detection. By grounding infrared representations in language semantics, JinSight enables a single model to generalize across heterogeneous infrared domains. We then introduce Latent Semantic Interaction (LSI), which exchanges language-aligned global semantics with fine-grained spatial features in a compact low-rank space. To address the lack of multimodal omni-domain IRST benchmarks, we build \textbf{OmniIRST-VL}, the first large-scale, highly diverse vision--language dataset for omni-domain IRST detection. It comprises over 39k annotations across six complementary instruction tasks covering both scene-level understanding and target-centric reasoning.

cs.CV

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1644ACL Anthology · ACL 2026

MUSEG: Reinforcing Video Temporal Understanding via Timestamp-Aware Multi-Segment Grounding

Fuwen Luo, Shengfeng Lou, Chi Chen, Ziyue Wang Tsinghua University, Tsinghua University, Chenliang Li Alibaba Group, Weizhou Shen, Jiyue Guo, Peng Li Tsinghua University 等 12 位作者

原文摘要

Video temporal understanding is crucial for multimodal large language models (MLLMs) to reason over events in videos. Despite recent advances in general video understanding, current MLLMs still struggle with fine-grained temporal reasoning. While reinforcement learning (RL) has been explored to address this issue recently, existing RL approaches remain limited in performance on time-sensitive tasks. In this work, we propose MUSEG, a novel RL-based method that enhances temporal understanding by introducing timestamp-aware multi-segment grounding. MUSEG enables MLLMs to align queries with multiple relevant video segments, promoting more comprehensive temporal reasoning. To facilitate effective learning, we design a customized RL training recipe with phased rewards that progressively guides the model toward temporally grounded reasoning. Extensive experiments on temporal grounding and time-sensitive video question answering (QA) tasks demonstrate that MUSEG significantly outperforms existing methods and generalizes well across diverse temporal understanding scenarios.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.620ACL Anthology · ACL 2026

Unleashing Spatial Reasoning in Multimodal Large Language Models via Textual Representation Guided Reasoning

Jiacheng Hua Tencent and Tsinghua University, Yishu Yin Tsinghua University, Yuhang Wu Tsinghua University, Tsinghua University, Tai Wang, Yifei Huang The University of Tokyo, Miao Liu Tsinghua University

原文摘要

Existing Multimodal Large Language Models (MLLMs) struggle with 3D spatial reasoning, as they fail to construct structured abstractions of the 3D environment depicted in video inputs. To bridge this gap, drawing inspiration from cognitive theories of allocentric spatial reasoning, we investigate how to enable MLLMs to model and reason over text-based spatial representations of video. Specifically, we introduce Textual Representation of Allocentric Context from Egocentric Video (TRACE), a prompting method that induces MLLMs to generate text-based representations of 3D environments as intermediate reasoning traces for more accurate spatial question answering. TRACE encodes meta-context, camera trajectories, and detailed object entities to support structured spatial reasoning over egocentric videos. Extensive experiments on VSI-Bench and OST-Bench demonstrate that TRACE yields notable and consistent improvements over prior prompting strategies across a diverse range of MLLM backbones, spanning different parameter scales and training schemas. We further present ablation studies to validate our design choices, along with detailed analyses that probe the bottlenecks of 3D spatial reasoning in MLLMs.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表