Summary of Modality Translation For Object Detection Adaptation Without Forgetting Prior Knowledge, by Heitor Rapela Medeiros et al.
Modality Translation for Object Detection Adaptation Without Forgetting Prior Knowledge
by Heitor Rapela Medeiros, Masih Aminbeidokhti, Fidel Guerrero Pena, David Latortue, Eric Granger, Marco Pedersoli
First submitted to arxiv on: 1 Apr 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 proposed Modality Translator (ModTr) model aims to adapt a large object detection model trained on RGB images to process new data extracted from IR images with a significant distribution shift. Unlike fine-tuning the original model, ModTr uses a small transformation network to directly minimize the detection loss and translate IR inputs to a format compatible with the original RGB model. This approach allows the original model to work without further changes or fine-tuning, achieving comparable or better performance on two benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a way to adapt an object detection model trained on one type of image (RGB) to work well with another type of image (IR) that is very different. This is useful for applications where you need to detect objects in images from multiple types, like visible light and infrared cameras. The researchers developed a new model called Modality Translator that can take IR images and translate them into RGB images so the original detection model can work with them. This approach worked well on two popular datasets and could be used in real-world applications. |
Keywords
» Artificial intelligence » Fine tuning » Object detection
