7 citations · 8 across the 13 of their papers we have counts for
14 papers
Evolutionary Computation for Trustworthy AI: From Attacks and Defenses to Self-Evolving Era
Junhao Dong, Chenkai Wang, Xuanhui Lin +8
As Artificial Intelligence (AI) has evolved from task-specific models to foundation models and agents, the scope of trustworthy AI has expanded from model-level robustness to the r…
RefCaptioner: Multi-Reference Image-Grounded Video Captioning
Tengfei Liu, Yang Shi, Yuran Wang +16
Existing video captioning models generate natural descriptions of video content but cannot explicitly ground local visual elements to multiple reference images. We introduce multi-…
AVE-Compass: Towards Holistic Evaluation for Audio-Video Editing Abilities
Yuqing Wen, Yukai Huang, Qianqian Xie +6
While instruction-based video editing has advanced rapidly, real-world videos contain tightly coupled audio and visual signals, and editing one modality often requires coordinated…
MultiRef-Compass: Towards Comprehensive Evaluation of Multi-Reference-to-Audio-Video Generation
Xiaohan Zhang, Yuqing Wen, Junlin Chen +9
Multi-reference-to-audio-video (MR2AV) generation aims to generate coherent audio-video content conditioned on multiple references and textual instructions. Existing benchmarks mai…
DR-Eval: Towards Realistic and Reproducible Deep Research Evaluation
Qianqian Xie, Qingheng Xiong, He Zhu +16
Deep Research Agents (DRAs) aim to solve complex, long-horizon research tasks involving planning, retrieval, multimodal understanding, and report generation, yet their evaluation r…
Robotic Scene Cloning:Advancing Zero-Shot Robotic Scene Adaptation in Manipulation via Visual Prompt Editing
Binyuan Huang, Yuqing Wen, Yucheng Zhao +5
Modern robots can perform a wide range of simple tasks and adapt to diverse scenarios in the well-trained environment. However, deploying pre-trained robot models in real-world use…
ManiAgent: An Agentic Framework for General Robotic Manipulation
Yi Yang, Kefan Gu, Yuqing Wen +4
While Vision-Language-Action (VLA) models have demonstrated impressive capabilities in robotic manipulation, their performance in complex reasoning and long-horizon task planning i…
IntentionVLA: Generalizable and Efficient Embodied Intention Reasoning for Human-Robot Interaction
Yandu Chen, Kefan Gu, Yuqing Wen +3
Vision-Language-Action (VLA) models leverage pretrained vision-language models (VLMs) to couple perception with robotic control, offering a promising path toward general-purpose em…
LLaDA-VLA: Vision Language Diffusion Action Models
Yuqing Wen, Hebei Li, Kefan Gu +3
The rapid progress of auto-regressive vision-language models (VLMs) has inspired growing interest in vision-language-action models (VLA) for robotic manipulation. Recently, masked…
ROSA: Harnessing Robot States for Vision-Language and Action Alignment
Yuqing Wen, Kefan Gu, Haoxuan Liu +4
Vision-Language-Action (VLA) models have recently made significant advance in multi-task, end-to-end robotic control, due to the strong generalization capabilities of Vision-Langua…
Panacea+: Panoramic and Controllable Video Generation for Autonomous Driving
Yuqing Wen, Yucheng Zhao, Yingfei Liu +7
The field of autonomous driving increasingly demands high-quality annotated video training data. In this paper, we propose Panacea+, a powerful and universally applicable framework…
SubjectDrive: Scaling Generative Data in Autonomous Driving via Subject Control
Binyuan Huang, Yuqing Wen, Yucheng Zhao +9
Autonomous driving progress relies on large-scale annotated datasets. In this work, we explore the potential of generative models to produce vast quantities of freely-labeled data…
Panacea: Panoramic and Controllable Video Generation for Autonomous Driving
Yuqing Wen, Yucheng Zhao, Yingfei Liu +7
The field of autonomous driving increasingly demands high-quality annotated training data. In this paper, we propose Panacea, an innovative approach to generate panoramic and contr…
ADriver-I: A General World Model for Autonomous Driving
Fan Jia, Weixin Mao, Yingfei Liu +5
Typically, autonomous driving adopts a modular design, which divides the full stack into perception, prediction, planning and control parts. Though interpretable, such modular desi…