5 citations · 6 across the 7 of their papers we have counts for
8 papers
Robot Manipulation with GPT-6-Astra: Body Knowledge, Experience Reuse, Emergent Skills, and Sim2Real Transfer
Sida He, Lingxi Xie, Yunning Cao +6
General-purpose multimodal agents can write robot-control programs, but repeated exploration and model-mediated action selection can make execution slow. We study how external body…
Robustifying Vision-Language Models via Test-Time Prompt Adaptation
Xingyu Zhu, Huanshen Wu, Shuo Wang +4
Pre-trained Vision-Language Models (VLMs) such as CLIP achieve strong zero-shot generalization, but their performance degrades sharply under adversarial perturbations. Existing tes…
Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas
Yuxuan Li, Lingxi Xie, Xinyue Huo +6
Long-form TV dramas present a formidable challenge for comprehensive video understanding, where deciphering complex storyline often relies on \textbf{speaker recognition}, the task…
SpaceVLLM: Endowing Multimodal Large Language Model with Spatio-Temporal Video Grounding Capability
Jiankang Wang, Zhihan Zhang, Zhihang Liu +4
Multimodal large language models (MLLMs) have made remarkable progress in either temporal or spatial localization. However, they struggle to perform spatio-temporal video grounding…
AlignZeg: Mitigating Objective Misalignment for Zero-shot Semantic Segmentation
Jiannan Ge, Lingxi Xie, Hongtao Xie +4
A serious issue that harms the performance of zero-shot visual recognition is named objective misalignment, i.e., the learning objective prioritizes improving the recognition accur…
Towards Balanced Alignment: Modal-Enhanced Semantic Modeling for Video Moment Retrieval
Zhihang Liu, Jun Li, Hongtao Xie +4
Video Moment Retrieval (VMR) aims to retrieve temporal segments in untrimmed videos corresponding to a given language query by constructing cross-modal alignment strategies. Howeve…
Dual-Stream Knowledge-Preserving Hashing for Unsupervised Video Retrieval
Pandeng Li, Hongtao Xie, Jiannan Ge +3
Unsupervised video hashing usually optimizes binary codes by learning to reconstruct input videos. Such reconstruction constraint spends much effort on frame-level temporal context…
Balanced Classification: A Unified Framework for Long-Tailed Object Detection
Tianhao Qi, Hongtao Xie, Pandeng Li +2
Conventional detectors suffer from performance degradation when dealing with long-tailed data due to a classification bias towards the majority head categories. In this paper, we c…