Summary of Ida-vlm: Towards Movie Understanding Via Id-aware Large Vision-language Model, by Yatai Ji et al.
IDA-VLM: Towards Movie Understanding via ID-Aware Large Vision-Language Model
by Yatai Ji, Shilong Zhang, Jie Wu, Peize Sun, Weifeng Chen, Xuefeng Xiao, Sidi Yang, Yujiu Yang, Ping Luo
First submitted to arxiv on: 10 Jul 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 rapid advancement of Large Vision-Language models (LVLMs) has demonstrated a spectrum of emergent capabilities, but current models only focus on single-scenario visual content. To explore the ability to associate instances across different scenes, we propose ID-Aware Large Vision-Language Model, IDA-VLM, and introduce a novel benchmark MM-ID for instance IDs memory and recognition across four dimensions: matching, location, question-answering, and captioning. Our findings highlight the limitations of existing LVLMs in recognizing and associating instance identities with ID reference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to make Large Vision-Language models better understand movies by helping them recognize characters across different scenes. To do this, they propose a new model called IDA-VLM and create a special benchmark to test its abilities. This is an important step towards making AI systems that can understand complex visual stories like movies. |
Keywords
» Artificial intelligence » Language model » Question answering
