1 citations · 2 across the 8 of their papers we have counts for
10 papers · 1 filter
MAVIS: Multi-Agent Video Retrieval via Structured Video Understanding
Jie Zhang, Qilang Ye, Hao Zhou +2
The dominant paradigm in video retrieval relies on embedding-based full-corpus scanning, which suffers from inherent computational inefficiency and the semantic asymmetry between i…
Retrieving to Recover: Towards Incomplete Audio-Visual Question Answering via Semantic-consistent Purification
Jiayu Zhang, Shuo Ye, Qilang Ye +3
Recent Audio-Visual Question Answering (AVQA) methods have advanced significantly. However, most AVQA methods lack effective mechanisms for handling missing modalities, suffering f…
SUGAR: Learning Skeleton Representation with Visual-Motion Knowledge for Action Recognition
Qilang Ye, Yu Zhou, Lian He +10
Large Language Models (LLMs) hold rich implicit knowledge and powerful transferability. In this paper, we explore the combination of LLMs with the human skeleton to perform action…
When Eyes and Ears Disagree: Can MLLMs Discern Audio-Visual Confusion?
Qilang Ye, Wei Zeng, Meng Liu +4
Can Multimodal Large Language Models (MLLMs) discern confused objects that are visually present but audio-absent? To study this, we introduce a new benchmark, AV-ConfuseBench, whic…
Task-Aware 3D Affordance Segmentation via 2D Guidance and Geometric Refinement
Lian He, Meng Liu, Qilang Ye +3
Understanding 3D scene-level affordances from natural language instructions is essential for enabling embodied agents to interact meaningfully in complex environments. However, thi…
IAD-GPT: Advancing Visual Knowledge in Multimodal Large Language Model for Industrial Anomaly Detection
Zewen Li, Zitong Yu, Qilang Ye +3
The robust causal capability of Multimodal Large Language Models (MLLMs) hold the potential of detecting defective objects in Industrial Anomaly Detection (IAD). However, most trad…
AV-Master: Dual-Path Comprehensive Perception Makes Better Audio-Visual Question Answering
Jiayu Zhang, Shuo Ye, Qilang Ye +3
Audio-Visual Question Answering (AVQA) requires models to effectively utilize both visual and auditory modalities to answer complex and diverse questions about audio-visual scenes.…
EMO-LLaMA: Enhancing Facial Emotion Understanding with Instruction Tuning
Bohao Xing, Zitong Yu, Xin Liu +6
Facial expression recognition (FER) is an important research topic in emotional artificial intelligence. In recent decades, researchers have made remarkable progress. However, curr…
Answering Diverse Questions via Text Attached with Key Audio-Visual Clues
Qilang Ye, Zitong Yu, Xin Liu
Audio-visual question answering (AVQA) requires reference to video content and auditory information, followed by correlating the question to predict the most precise answer. Althou…
CAT: Enhancing Multimodal Large Language Model to Answer Questions in Dynamic Audio-Visual Scenarios
Qilang Ye, Zitong Yu, Rui Shao +3
This paper focuses on the challenge of answering questions in scenarios that are composed of rich and complex dynamic audio-visual components. Although existing Multimodal Large La…