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20242026
most citedA Survey of Embodied Learning for Object-Centric Robotic Manipulation

16 citations · 16 across the 19 of their papers we have counts for

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13 papers · 1 filter

cs.RO2026

Physics-Aware Object-Centric SE(3) Forecasting for Real-Time Proactive Dynamic Grasping

Yirui Zhou, Hongli Xu, Ying Zheng +2

Proactive dynamic grasping requires robots to predict the future state at which physical contact will occur, rather than reacting to the currently observed object pose. But what fu…

cs.CV2026

SeqAlign3DVG: A Sequence-Aligned Benchmark and Voxel Reasoning Framework for 3D Visual Grounding

Yi Zhang, Yi Wang, Yueting Wu +3

Image-based 3D visual grounding is critical for embodied agents, yet existing benchmarks suffer from loose text-observation alignment and neglect temporal ordering. We introduce Se…

cs.CV2026

TextDS: Parameter-Efficient Representation Alignment for Scene Text Detection under Distribution Shifts

Boyuan Chen, Zichen Dang, Chuang Yang +2

In real-world deployments, scene text detectors inevitably face distribution shifts beyond the training distribution. Prior work often depends on large-scale scene-text pretraining…

cs.CR2026

EVA: Editing for Versatile Alignment against Jailbreaks

Yi Wang, Hongye Qiu, Yue Xu +4

Large Language Models (LLMs) and Vision Language Models (VLMs) have demonstrated impressive capabilities but remain vulnerable to jailbreaking attacks, where adversaries exploit te…

cs.CV2026

EARL: Towards a Unified Analysis-Guided Reinforcement Learning Framework for Egocentric Interaction Reasoning and Pixel Grounding

Yuejiao Su, Xinshen Zhang, Zhen Ye +3

Understanding human--environment interactions from egocentric vision is essential for assistive robotics and embodied intelligent agents, yet existing multimodal large language mod…

cs.CV2026

Qwen-Image-VAE-2.0 Technical Report

Zekai Zhang, Deqing Li, Kuan Cao +27

We present Qwen-Image-VAE-2.0, a suite of high-compression Variational Autoencoders (VAEs) that achieve significant advances in both reconstruction fidelity and diffusability. To a…

cs.CV2026

Qwen-Image-2.0 Technical Report

Bing Zhao, Chenfei Wu, Deqing Li +72

We present Qwen-Image-2.0, an omni-capable image generation foundation model that unifies high-fidelity generation and precise image editing within a single framework. Despite rece…

cs.CL2026

LLM-Agnostic Semantic Representation Attack

Jiawei Lian, Jianhong Pan, Lefan Wang +4

Large Language Models (LLMs) increasingly employ alignment techniques to prevent harmful outputs. Despite these safeguards, attackers can circumvent them by crafting adversarial pr…

cs.CV2026

Evolution-Inspired Sample Competition for Deep Neural Network Optimization

Ying Zheng, Yiyi Zhang, Yi Wang +1

Conventional deep network training generally optimizes all samples under a largely uniform learning paradigm, without explicitly modeling the heterogeneous competition among them.…

cs.IR2026

SID-Coord: Coordinating Semantic IDs for ID-based Ranking in Short-Video Search

Guowen Li, Yuepeng Zhang, Shunyu Zhang +4

Large-scale short-video search ranking models are typically trained on sparse co-occurrence signals over hashed item identifiers (HIDs). While effective at memorizing frequent inte…

cs.CV2026

MASS: Mesh-inellipse Aligned Deformable Surfel Splatting for Hand Reconstruction and Rendering from Egocentric Monocular Video

Haoyu Zhu, Yi Zhang, Lei Yao +2

Reconstructing high-fidelity 3D hands from egocentric monocular videos remains a challenge due to the limitations in capturing high-resolution geometry, hand-object interactions, a…

cs.CV2026

HVG-3D: Bridging Real and Simulation Domains for 3D-Conditional Hand-Object Interaction Video Synthesis

Mingjin Chen, Junhao Chen, Zhaoxin Fan +6

Recent methods have made notable progress in the visual quality of hand-object interaction video synthesis. However, most approaches rely on 2D control signals that lack spatial ex…

cs.CV2026

ProCal: Probability Calibration for Neighborhood-Guided Source-Free Domain Adaptation

Ying Zheng, Yiyi Zhang, Yi Wang +1

Source-Free Domain Adaptation (SFDA) adapts pre-trained models to unlabeled target domains without requiring access to source data. Although state-of-the-art methods leveraging loc…