Rapid development of large language models (LLMs) has significantly advanced multimodal large language models (LMMs), particularly in vision-language tasks. However, existing video-language models often overlook precise temporal localization and struggle with videos of varying lengths. We introduce TimeMarker, a versatile Video-LLM designed for high-quality dialogue based on video content, emphasizing temporal localization. TimeMarker integrates Temporal Separator Tokens to enhance temporal awareness, accurately marking specific moments within videos. It employs the AnyLength mechanism for dynamic frame sampling and adaptive token merging, enabling effective handling of both short and long videos. Additionally, TimeMarker utilizes diverse datasets, including further transformed temporal-related video QA datasets, to bolster its temporal understanding capabilities. Image and interleaved data are also employed to further enhance the model's semantic perception ability. Evaluations demonstrate that TimeMarker achieves state-of-the-art performance across multiple benchmarks, excelling in both short and long video categories. Our project page is at \url{https://github.com/TimeMarker-LLM/TimeMarker/}.

本研究解决了现有视频语言模型在精确时间定位方面的不足，以应对不同长度视频的挑战。我们提出的时间标记器通过引入时间分隔符标记增强时间意识，并采用AnyLength机制来适应性地处理短视频和长视频。评估结果表明，时间标记器在多个基准测试中表现出色，展示了其在视频理解领域的显著潜力。

时间标记器：一种具有优越时间定位能力的多用途视频大语言模型