Summary of Align and Distill: Unifying and Improving Domain Adaptive Object Detection, by Justin Kay et al.
Align and Distill: Unifying and Improving Domain Adaptive Object Detection
by Justin Kay, Timm Haucke, Suzanne Stathatos, Siqi Deng, Erik Young, Pietro Perona, Sara Beery, Grant Van Horn
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 addresses the issue of domain adaptive object detection (DAOD) methods performing poorly on unseen data. It highlights systemic benchmarking pitfalls in past studies, including overestimation, inconsistent implementation practices, and lack of generality due to outdated backbones and limited diversity in benchmarks. The authors introduce a unified framework, Align and Distill (ALDI), for fair comparison of DAOD methods, along with a new training and evaluation protocol, and a benchmark dataset, CFC-DAOD. They also propose a new state-of-the-art method, ALDI++, which achieves improved results on various benchmarks. The framework, dataset, and method are architecture-agnostic and outperform previous methods without additional tuning. This work sets a new standard for DAOD research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computer vision models better at recognizing objects in different environments. Right now, these models often struggle when they’re shown pictures from places they weren’t trained on. The authors found that previous studies had some big flaws, like not using strong enough “baselines” (the simplest way a model can solve the problem) and not being clear about how they tested their methods. They created a new system called ALDI to fix these problems and make it easier to compare different models. They also made a new dataset with lots of different pictures to test their method on. The result is a better model that works well on many different types of images. |
Keywords
* Artificial intelligence * Object detection
