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TimeSuite: Improving MLLMs for Long Video Understanding via Grounded Tuning

Autor: Zeng, Xiangyu, Li, Kunchang, Wang, Chenting, Li, Xinhao, Jiang, Tianxiang, Yan, Ziang, Li, Songze, Shi, Yansong, Yue, Zhengrong, Wang, Yi, Wang, Yali, Qiao, Yu, Wang, Limin

Multimodal Large Language Models (MLLMs) have demonstrated impressive performance in short video understanding. However, understanding long-form videos still remains challenging for MLLMs. This paper proposes TimeSuite, a collection of new designs to

Externí odkaz: http://arxiv.org/abs/2410.19702

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InternVideo2: Scaling Foundation Models for Multimodal Video Understanding

We introduce InternVideo2, a new family of video foundation models (ViFM) that achieve the state-of-the-art results in video recognition, video-text tasks, and video-centric dialogue. Our core design is a progressive training approach that unifies th

Externí odkaz: http://arxiv.org/abs/2403.15377

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Collaboration and Transition: Distilling Item Transitions into Multi-Query Self-Attention for Sequential Recommendation

Autor: Zhu, Tianyu, Shi, Yansong, Zhang, Yuan, Wu, Yihong, Mo, Fengran, Nie, Jian-Yun

Modern recommender systems employ various sequential modules such as self-attention to learn dynamic user interests. However, these methods are less effective in capturing collaborative and transitional signals within user interaction sequences. Firs

Externí odkaz: http://arxiv.org/abs/2311.01056

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