2 citations · 4 across the 6 of their papers we have counts for
6 papers · 1 filter
PCA: Persistence-Aware Compression and Aggregation for Fast Video Large Language Models
Zihan Song, Shuo Ye, Bo Zhao +4
Despite advances in Video Large Language Models (VLLMs) that have displayed promising outcomes in video understanding, the redundancy in the long-duration frames remains a hindranc…
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…
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.…
Grounding-Prompter: Prompting LLM with Multimodal Information for Temporal Sentence Grounding in Long Videos
Houlun Chen, Xin Wang, Hong Chen +3
Temporal Sentence Grounding (TSG), which aims to localize moments from videos based on the given natural language queries, has attracted widespread attention. Existing works are ma…
LLM4VG: Large Language Models Evaluation for Video Grounding
Wei Feng, Xin Wang, Hong Chen +7
Recently, researchers have attempted to investigate the capability of LLMs in handling videos and proposed several video LLM models. However, the ability of LLMs to handle video gr…
VTimeLLM: Empower LLM to Grasp Video Moments
Bin Huang, Xin Wang, Hong Chen +2
Large language models (LLMs) have shown remarkable text understanding capabilities, which have been extended as Video LLMs to handle video data for comprehending visual details. Ho…