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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.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.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.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.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…

cs.CV2025

ANNEXE: Unified Analyzing, Answering, and Pixel Grounding for Egocentric Interaction

Yuejiao Su, Yi Wang, Qiongyang Hu +2

Egocentric interaction perception is one of the essential branches in investigating human-environment interaction, which lays the basis for developing next-generation intelligent s…

cs.CV2024

SignEye: Traffic Sign Interpretation from Vehicle First-Person View

Chuang Yang, Xu Han, Tao Han +5

Traffic signs play a key role in assisting autonomous driving systems (ADS) by enabling the assessment of vehicle behavior in compliance with traffic regulations and providing navi…

cs.CV2024

Attack Anything: Blind DNNs via Universal Background Adversarial Attack

Jiawei Lian, Shaohui Mei, Xiaofei Wang +5

It has been widely substantiated that deep neural networks (DNNs) are susceptible and vulnerable to adversarial perturbations. Existing studies mainly focus on performing attacks b…

cs.CV2024

PADetBench: Towards Benchmarking Physical Attacks against Object Detection

Jiawei Lian, Jianhong Pan, Lefan Wang +3

Physical attacks against object detection have gained increasing attention due to their significant practical implications. However, conducting physical experiments is extremely ti…