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cs.CV2024
Exploring the Design Space of Visual Context Representation in Video MLLMs
Yifan Du, Yuqi Huo, Kun Zhou +7
Video Multimodal Large Language Models (MLLMs) have shown remarkable capability of understanding the video semantics on various downstream tasks. Despite the advancements, there is…
cs.CV2024
Towards Event-oriented Long Video Understanding
Yifan Du, Kun Zhou, Yuqi Huo +7
With the rapid development of video Multimodal Large Language Models (MLLMs), numerous benchmarks have been proposed to assess their video understanding capability. However, due to…
cs.CV2024
Needle In A Video Haystack: A Scalable Synthetic Evaluator for Video MLLMs
Zijia Zhao, Haoyu Lu, Yuqi Huo +6
Video understanding is a crucial next step for multimodal large language models (MLLMs). Various benchmarks are introduced for better evaluating the MLLMs. Nevertheless, current vi…