2 citations · 6 across the 13 of their papers we have counts for
7 papers · 1 filter
A Skill-augmented Agentic Framework and Benchmark for Multi-Video Understanding
Yue Zhang, Liqiang Jing, Jia Li +4
Multimodal Large Language Models have achieved strong performance in single-video understanding, yet their ability to reason across multiple videos remains limited. Existing approa…
From Waveforms to Pixels: A Survey on Audio-Visual Segmentation
Jia Li, Yapeng Tian
Audio-Visual Segmentation (AVS) aims to identify and segment sound-producing objects in videos by leveraging both visual and audio modalities. It has emerged as a significant resea…
Continual Audio-Visual Sound Separation
Weiguo Pian, Yiyang Nan, Shijian Deng +3
In this paper, we introduce a novel continual audio-visual sound separation task, aiming to continuously separate sound sources for new classes while preserving performance on prev…
MA-AVT: Modality Alignment for Parameter-Efficient Audio-Visual Transformers
Tanvir Mahmud, Shentong Mo, Yapeng Tian +1
Recent advances in pre-trained vision transformers have shown promise in parameter-efficient audio-visual learning without audio pre-training. However, few studies have investigate…
Scaling Diffusion Mamba with Bidirectional SSMs for Efficient Image and Video Generation
Shentong Mo, Yapeng Tian
In recent developments, the Mamba architecture, known for its selective state space approach, has shown potential in the efficient modeling of long sequences. However, its applicat…
T-VSL: Text-Guided Visual Sound Source Localization in Mixtures
Tanvir Mahmud, Yapeng Tian, Diana Marculescu
Visual sound source localization poses a significant challenge in identifying the semantic region of each sounding source within a video. Existing self-supervised and weakly superv…
Class-Incremental Grouping Network for Continual Audio-Visual Learning
Shentong Mo, Weiguo Pian, Yapeng Tian
Continual learning is a challenging problem in which models need to be trained on non-stationary data across sequential tasks for class-incremental learning. While previous methods…