7 papers
Stage-adaptive Token Selection for Efficient Omni-modal LLMs
Zijie Xin, Jie Yang, Ruixiang Zhao +4
The unified audio-visual understanding capability of omni-modal large language models (om-LLMs) comes at the high cost of processing tens of thousands of visual and audio tokens th…
OmniPro: A Comprehensive Benchmark for Omni-Proactive Streaming Video Understanding
Ruixiang Zhao, Jie Yang, Zijie Xin +4
Omni-proactive streaming video understanding, i.e., autonomously deciding when to speak and what to say from continuous audio-visual streams, is an emerging capability of omni-moda…
SAVE: Speech-Aware Video Representation Learning for Video-Text Retrieval
Ruixiang Zhao, Zhihao Xu, Bangxiang Lan +3
For video-text retrieval, the use of CLIP has been a de facto choice. Since CLIP provides only image and text encoders, this consensus has led to a biased paradigm that entirely ig…
Hybrid-Tower: Fine-grained Pseudo-query Interaction and Generation for Text-to-Video Retrieval
Bangxiang Lan, Ruobing Xie, Ruixiang Zhao +4
The Text-to-Video Retrieval (T2VR) task aims to retrieve unlabeled videos by textual queries with the same semantic meanings. Recent CLIP-based approaches have explored two framewo…
Multi-Object Sketch Animation by Scene Decomposition and Motion Planning
Jingyu Liu, Zijie Xin, Yuhan Fu +3
Sketch animation, which brings static sketches to life by generating dynamic video sequences, has found widespread applications in GIF design, cartoon production, and daily enterta…
ASR-enhanced Multimodal Representation Learning for Cross-Domain Product Retrieval
Ruixiang Zhao, Jian Jia, Yan Li +5
E-commerce is increasingly multimedia-enriched, with products exhibited in a broad-domain manner as images, short videos, or live stream promotions. A unified and vectorized cross-…
Co-Teaching for Unsupervised Domain Adaptation and Expansion
Hailan Lin, Qijie Wei, Kaibin Tian +2
Unsupervised Domain Adaptation (UDA) essentially trades a model's performance on a source domain for improving its performance on a target domain. To overcome this, Unsupervised Do…