5 papers · 1 filter
Small Vision-Language Models are Smart Compressors for Long Video Understanding
Junjie Fei, Jun Chen, Zechun Liu +13
Adapting Multimodal Large Language Models (MLLMs) for hour-long videos is bottlenecked by context limits. Dense visual streams saturate token budgets and exacerbate the lost-in-the…
Vgent: Graph-based Retrieval-Reasoning-Augmented Generation For Long Video Understanding
Xiaoqian Shen, Wenxuan Zhang, Jun Chen +1
Understanding and reasoning over long videos pose significant challenges for large video language models (LVLMs) due to the difficulty in processing intensive video tokens beyond c…
Neural Catalog: Scaling Species Recognition with Catalog of Life-Augmented Generation
Faizan Farooq Khan, Jun Chen, Youssef Mohamed +2
Open-vocabulary species recognition is a major challenge in computer vision, particularly in ornithology, where new taxa are continually discovered. While benchmarks like CUB-200-2…
WikiAutoGen: Towards Multi-Modal Wikipedia-Style Article Generation
Zhongyu Yang, Jun Chen, Dannong Xu +5
Knowledge discovery and collection are intelligence-intensive tasks that traditionally require significant human effort to ensure high-quality outputs. Recent research has explored…
4D-Bench: Benchmarking Multi-modal Large Language Models for 4D Object Understanding
Wenxuan Zhu, Bing Li, Cheng Zheng +8
Multimodal Large Language Models (MLLMs) have demonstrated impressive 2D image/video understanding capabilities. However, there are no publicly standardized benchmarks to assess th…