E.T. Bench: Towards Open-Ended Event-Level Video-Language Understanding
Authors: name: Ye Liu, name: Zongyang Ma, name: Zhongang Qi, name: Yang Wu, name: Ying Shan, name: Chang Wen Chen

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.