Summary of Timemarker: a Versatile Video-llm For Long and Short Video Understanding with Superior Temporal Localization Ability, by Shimin Chen et al.
TimeMarker: A Versatile Video-LLM for Long and Short Video Understanding with Superior Temporal Localization Ability
by Shimin Chen, Xiaohan Lan, Yitian Yuan, Zequn Jie, Lin Ma
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces TimeMarker, a versatile video-language model designed for high-quality dialogue based on video content. It emphasizes temporal localization and addresses the limitations of existing models in handling videos of varying lengths. TimeMarker employs Temporal Separator Tokens to enhance temporal awareness and accurately mark specific moments within videos. The model also utilizes adaptive token merging and dynamic frame sampling through its AnyLength mechanism, enabling effective handling of both short and long videos. Additionally, TimeMarker leverages diverse datasets, including transformed video QA datasets, to improve its temporal understanding capabilities. Image and interleaved data are also used to 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new type of computer program called TimeMarker that can understand videos and talk about what’s happening at specific moments. The program is good at figuring out the timing of events in videos, even if they’re different lengths. It uses special tokens to mark important moments and can adjust its thinking based on the video’s length. TimeMarker also learns from a variety of datasets and gets help from images and other types of data. When tested, TimeMarker did very well at understanding videos and talking about what it saw. |
Keywords
» Artificial intelligence » Language model » Token
