2 citations · 3 across the 13 of their papers we have counts for
15 papers
VideoSlate: Stateful Commit Control for Temporally Grounded Agentic Video Reasoning
Shuo Zheng, Yuanjie Dang, Peng Chen +4
Agentic video reasoning systems gather and assess video evidence across stages, selecting relevant spans as visual evidence for answering questions. New candidates and observations…
SAVU-BENCH: A Real-World Benchmark for Spatial Audio-Visual Understanding
Yu Chen, Ruihang Liu, Yangguang Xu +5
Spatial audio-visual understanding requires models to recognize not only what is present, but also where events occur and how they relate across modalities. Existing benchmarks oft…
Boot-and-Feedback Framework for Generalist-Expert Model Collaboration in Breast Ultrasound Diagnosis
Ming Cheng, Hongyu Sun, Zhaolin Chen +3
Breast ultrasound (BUS) is widely used for breast cancer diagnosis yet remains operator-dependent. While deep learning shows promise, ensuring diagnostic reliability and interpreta…
SkelHCC: A Hyperbolic CLIP-Driven Cache Adaptation Framework for Skeleton-based One-Shot Action Recognition
Yanan Liu, Anqi Zhu, Jingmin Zhu +6
Skeleton-based action recognition aims to understand human behaviors from body joint sequences and is especially challenging in the one-shot setting, where only a single labeled ex…
Translating Signals to Languages for sEMG-Based Activity Recognition
Ming Wang, Haoxuan Qu, Qiuhong Ke +3
Surface electromyography (sEMG) signal-based activity recognition has attracted increasing research attention in recent years. To develop accurate sEMG signal-based activity recogn…
When Rewards Disagree: Reward Weighting for Token-Efficient Video Reasoning
Zinuo Li, Yongxin Guo, Jun Liu +7
Human perception interprets video dynamics by constructing structured mental representations of entities, actions, and temporal boundaries before engaging in high-level reasoning.…
ST-GDance++: A Scalable Spatial-Temporal Diffusion for Long-Duration Group Choreography
Jing Xu, Weiqiang Wang, Cunjian Chen +2
Group dance generation from music requires synchronizing multiple dancers while maintaining spatial coordination, making it highly relevant to applications such as film production,…
TSkel-Mamba: Temporal Dynamic Modeling via State Space Model for Human Skeleton-based Action Recognition
Yanan Liu, Jun Liu, Hao Zhang +4
Skeleton-based action recognition has garnered significant attention in the computer vision community. Inspired by the recent success of the selective state-space model (SSM) Mamba…
Boosting Skeleton-based Zero-Shot Action Recognition with Training-Free Test-Time Adaptation
Jingmin Zhu, Anqi Zhu, Hossein Rahmani +3
We introduce Skeleton-Cache, the first training-free test-time adaptation framework for skeleton-based zero-shot action recognition (SZAR), aimed at improving model generalization…
DynaPURLS: Dynamic Refinement of Part-Aware Representations for Skeleton-Based Zero-Shot Action Recognition
Jingmin Zhu, Anqi Zhu, James Bailey +5
Zero-shot skeleton-based action recognition (ZS-SAR) is fundamentally constrained by prevailing approaches that rely on aligning skeleton features with static, class-level semantic…
TSTMotion: Training-free Scene-aware Text-to-motion Generation
Ziyan Guo, Haoxuan Qu, Hossein Rahmani +4
Text-to-motion generation has recently garnered significant research interest, primarily focusing on generating human motion sequences in blank backgrounds. However, human motions…
LongDiff: Training-Free Long Video Generation in One Go
Zhuoling Li, Hossein Rahmani, Qiuhong Ke +1
Video diffusion models have recently achieved remarkable results in video generation. Despite their encouraging performance, most of these models are mainly designed and trained fo…
Unified Prompt Attack Against Text-to-Image Generation Models
Duo Peng, Qiuhong Ke, Mark He Huang +2
Text-to-Image (T2I) models have advanced significantly, but their growing popularity raises security concerns due to their potential to generate harmful images. To address these is…
UPAM: Unified Prompt Attack in Text-to-Image Generation Models Against Both Textual Filters and Visual Checkers
Duo Peng, Qiuhong Ke, Jun Liu
Text-to-Image (T2I) models have raised security concerns due to their potential to generate inappropriate or harmful images. In this paper, we propose UPAM, a novel framework that…
NEURAL MARIONETTE: A Transformer-based Multi-action Human Motion Synthesis System
Weiqiang Wang, Xuefei Zhe, Qiuhong Ke +4
We present a neural network-based system for long-term, multi-action human motion synthesis. The system, dubbed as NEURAL MARIONETTE, can produce high-quality and meaningful motion…