Summary of D3t: Distinctive Dual-domain Teacher Zigzagging Across Rgb-thermal Gap For Domain-adaptive Object Detection, by Dinh Phat Do et al.
D3T: Distinctive Dual-Domain Teacher Zigzagging Across RGB-Thermal Gap for Domain-Adaptive Object Detection
by Dinh Phat Do, Taehoon Kim, Jaemin Na, Jiwon Kim, Keonho Lee, Kyunghwan Cho, Wonjun Hwang
First submitted to arxiv on: 14 Mar 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 A novel domain adaptation framework for object detection is proposed to bridge the significant gap between visible and thermal domains. The Distinctive Dual-Domain Teacher (D3T) framework utilizes distinct training paradigms for each domain, segregating source and target sets to build dual-teachers. Exponential moving average is applied to the student model, with a zigzag learning method facilitating gradual transition from visible to thermal during training. Experimental protocols using FLIR and KAIST datasets demonstrate the superior performance of D3T. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to help computers detect objects in thermal images is being developed. Right now, it’s hard for computers to learn from one type of image (like daytime pictures) and apply that learning to a very different type of image (like thermal night vision). The proposed D3T framework helps bridge this gap by using two separate “teachers” to teach the computer, with an extra step to help the computer smoothly switch from visible to thermal images. This new method is tested on real-world thermal datasets and shows great results. |
Keywords
» Artificial intelligence » Domain adaptation » Object detection » Student model
