Summary of Trafficvlm: a Controllable Visual Language Model For Traffic Video Captioning, by Quang Minh Dinh et al.
TrafficVLM: A Controllable Visual Language Model for Traffic Video Captioning
by Quang Minh Dinh, Minh Khoi Ho, Anh Quan Dang, Hung Phong Tran
First submitted to arxiv on: 14 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 presents TrafficVLM, a novel multi-modal dense video captioning model that generates long fine-grained descriptions of vehicle and pedestrian behavior in traffic events. The model analyzes traffic videos at different levels, both spatially and temporally, to provide detailed descriptions of the event. A conditional component is proposed to control the generation outputs, and a multi-task fine-tuning paradigm is used to enhance learning capability. Experimental results show that TrafficVLM performs well on both vehicle and overhead camera views, achieving outstanding results in Track 2 of the AI City Challenge 2024. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Traffic video description and analysis are important for urban surveillance systems. This paper presents a new way to understand traffic events by generating detailed descriptions of what’s happening. The model looks at videos from different angles and times, and it can describe what vehicles and pedestrians are doing during the event. The team also adds special controls to help the model learn better. They tested their idea and got great results in a competition. |
Keywords
» Artificial intelligence » Fine tuning » Multi modal » Multi task
