E.T. Bench: Towards Open-Ended Event-Level Video-Language Understanding

• URL: http://arxiv.org/abs/2409.18111v1
• Date: Thu, 26 Sep 2024 17:53:04 GMT
• Title: E.T. Bench: Towards Open-Ended Event-Level Video-Language Understanding
• Authors: Ye Liu, Zongyang Ma, Zhongang Qi, Yang Wu, Ying Shan, Chang Wen Chen,
• Abstract summary: We introduce E.T. Bench (Event-Level & Time-Sensitive Video Understanding Benchmark), a large-scale benchmark for open-ended event-level video understanding. We extensively evaluated 8 Image-LLMs and 12 Video-LLMs on our benchmark, and the results reveal that state-of-the-art models for coarse-level (video-level) understanding struggle to solve our fine-grained tasks. Our simple but effective solution demonstrates superior performance in multiple scenarios.
• Score: 57.630136434038384
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent advances in Video Large Language Models (Video-LLMs) have demonstrated their great potential in general-purpose video understanding. To verify the significance of these models, a number of benchmarks have been proposed to diagnose their capabilities in different scenarios. However, existing benchmarks merely evaluate models through video-level question-answering, lacking fine-grained event-level assessment and task diversity. To fill this gap, we introduce E.T. Bench (Event-Level & Time-Sensitive Video Understanding Benchmark), a large-scale and high-quality benchmark for open-ended event-level video understanding. Categorized within a 3-level task taxonomy, E.T. Bench encompasses 7.3K samples under 12 tasks with 7K videos (251.4h total length) under 8 domains, providing comprehensive evaluations. We extensively evaluated 8 Image-LLMs and 12 Video-LLMs on our benchmark, and the results reveal that state-of-the-art models for coarse-level (video-level) understanding struggle to solve our fine-grained tasks, e.g., grounding event-of-interests within videos, largely due to the short video context length, improper time representations, and lack of multi-event training data. Focusing on these issues, we further propose a strong baseline model, E.T. Chat, together with an instruction-tuning dataset E.T. Instruct 164K tailored for fine-grained event-level understanding. Our simple but effective solution demonstrates superior performance in multiple scenarios.

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