Summary of Attention-based Class-conditioned Alignment For Multi-source Domain Adaptation Of Object Detectors, by Atif Belal and Akhil Meethal and Francisco Perdigon Romero and Marco Pedersoli and Eric Granger
Attention-based Class-Conditioned Alignment for Multi-Source Domain Adaptation of Object Detectors
by Atif Belal, Akhil Meethal, Francisco Perdigon Romero, Marco Pedersoli, Eric Granger
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 This paper proposes an attention-based class-conditioned alignment method for multi-source domain adaptation (MSDA) in object detection. The goal is to mitigate the impact of distribution shifts by promoting feature alignment across source and target domains. The proposed approach, which combines an attention module with an adversarial domain classifier, learns domain-invariant and class-specific instance representations. This method outperforms state-of-the-art MSDA methods on multiple benchmarking datasets and exhibits robustness to class imbalance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn to detect objects better by sharing knowledge from different sources. When pictures look very different between training and testing, this method can adapt to the change. It’s like a translator that helps the machine understand the same object in different languages. The new approach is more accurate and reliable than existing methods, even when some classes are harder to learn than others. |
Keywords
* Artificial intelligence * Alignment * Attention * Domain adaptation * Object detection
