Summary of Pipa++: Towards Unification Of Domain Adaptive Semantic Segmentation Via Self-supervised Learning, by Mu Chen and Zhedong Zheng and Yi Yang
PiPa++: Towards Unification of Domain Adaptive Semantic Segmentation via Self-supervised Learning
by Mu Chen, Zhedong Zheng, Yi Yang
First submitted to arxiv on: 24 Jul 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 unsupervised domain adaptive segmentation approach aims to improve model accuracy on target domains without relying on labeled data from those domains. This is crucial when labeled target domain data is scarce or unavailable. The goal is to align feature representations of source and target domains, enabling the model to generalize well to the target domain. Current image- and video-level domain adaptation have been addressed using different frameworks, training strategies, and optimizations despite their underlying connections. This paper proposes a unified framework PiPa++, which leverages the core idea of comparing to (1) encourage learning of discriminative pixel-wise features with intraclass compactness and inter-class separability, (2) promote robust feature learning of identical patches against different contexts or fluctuations, and (3) enable learning of temporal continuity under dynamic environments. With a designed task-smart contrastive sampling strategy, PiPa++ enables mining of more informative training samples according to the task demand. Extensive experiments demonstrate effectiveness on both image-level and video-level domain adaptation benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Unsupervised domain adaptive segmentation helps models get better at recognizing things in new situations without needing labeled data from those situations. This is important when we don’t have enough labeled data for a specific situation. The goal is to make the model’s features match up between the old and new situations, so it can recognize things correctly in the new situation. Right now, different approaches are being used for image-level and video-level domain adaptation, even though they’re connected. This paper proposes a single approach called PiPa++ that compares different things to (1) help the model learn specific features, (2) make those features more robust, and (3) enable the model to understand how things change over time. By using this approach, we can get better results on both image-level and video-level domain adaptation tasks. |
Keywords
» Artificial intelligence » Domain adaptation » Unsupervised
