1 citations · 1 across the 8 of their papers we have counts for
9 papers
LIBERO-VIFO: Benchmarking the Capability and Safety of Visual Cue Following in Vision-Language-Action Models
Zhengyan Qian, Rui Yan, Alex Jinpeng Wang +1
Visual cues are increasingly adopted to guide robot learning, but whether Vision-Language-Action (VLA) models can reliably follow authorized cues while disregarding unauthorized on…
Spatio-temporal Decoupled Knowledge Compensator for Few-Shot Action Recognition
Hongyu Qu, Xiangbo Shu, Rui Yan +3
Few-Shot Action Recognition (FSAR) is a challenging task that requires recognizing novel action categories with a few labeled videos. Recent works typically apply semantically coar…
Spatiotemporal-Untrammelled Mixture of Experts for Multi-Person Motion Prediction
Zheng Yin, Chengjian Li, Xiangbo Shu +3
Comprehensively and flexibly capturing the complex spatio-temporal dependencies of human motion is critical for multi-person motion prediction. Existing methods grapple with two pr…
ASTRA: Let Arbitrary Subjects Transform in Video Editing
Fei Shen, Weihao Xu, Rui Yan +4
While existing video editing methods excel with single subjects, they struggle in dense, multi-subject scenes, frequently suffering from attention dilution and mask boundary entang…
TEST-V: TEst-time Support-set Tuning for Zero-shot Video Classification
Rui Yan, Jin Wang, Hongyu Qu +4
Recently, adapting Vision Language Models (VLMs) to zero-shot visual classification by tuning class embedding with a few prompts (Test-time Prompt Tuning, TPT) or replacing class n…
Vision-centric Token Compression in Large Language Model
Ling Xing, Alex Jinpeng Wang, Rui Yan +2
Real-world applications are stretching context windows to hundreds of thousand of tokens while Large Language Models (LLMs) swell from billions to trillions of parameters. This dua…
EventCrab: Harnessing Frame and Point Synergy for Event-based Action Recognition and Beyond
Meiqi Cao, Xiangbo Shu, Jiachao Zhang +3
Event-based Action Recognition (EAR) possesses the advantages of high-temporal resolution capturing and privacy preservation compared with traditional action recognition. Current l…
FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data
Binqian Xu, Xiangbo Shu, Haiyang Mei +3
Multimodal Large Language Models (MLLMs) have made significant advancements, demonstrating powerful capabilities in processing and understanding multimodal data. Fine-tuning MLLMs…
Locality-aware Cross-modal Correspondence Learning for Dense Audio-Visual Events Localization
Ling Xing, Hongyu Qu, Rui Yan +2
Dense-localization Audio-Visual Events (DAVE) aims to identify time boundaries and corresponding categories for events that are both audible and visible in a long video, where even…