CoinSeg: Contrast Inter- and Intra- Class Representations for Incremental Segmentation

By Zekang Zhang, Guangyu Gao, Jianbo Jiao, Chi Harold Liu, Yunchao Wei. 2023

37 citations recorded by the source corpus.

Class incremental semantic segmentation aims to strike a balance between the model’s stability and plasticity by maintaining old knowledge while adapting to new concepts. However, most state-of-the-art methods use the freeze strategy for stability, which compromises the model’s plasticity. In contrast, releasing parameter training for plasticity could lead to the best performance for all categories, but this requires discriminative feature representation. Therefore, we prioritize the model’s plasticity and propose the Contrast inter- and intra-class representations for Incremental Segmentation (CoinSeg), which pursues discriminative representations for flexible parameter tuning. Inspired by the Gaussian mixture model that samples from a mixture of Gaussian distributions, CoinSeg emphasizes intra-class diversity with multiple contrastive representation centroids. Specifically, we use mask proposals to identify regions with strong objectness that are likely to be diverse instances/centroids of a category. These mask proposals are then used for contrastive representations to reinforce intra-class diversity. Meanwhile, to avoid bias from intra-class diversity, we also apply category-level pseudo-labels to enhance category-level consistency and inter-category diversity. Additionally, CoinSeg ensures the model’s stability and alleviates forgetting through a specific flexible tuning strategy. We validate CoinSeg on Pascal VOC 2012 and ADE20K datasets with multiple incremental scenarios and achieve superior results compared to previous state-of-the-art methods, especially in more challenging and realistic long-term scenarios. Code is available at https://github.com/zkzhang98/CoinSeg.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yunchao Wei
2. Guoliang Kang
3. Gengwei Zhang
4. Liyuan Wang
5. Ling Chen
6. Yi Yang
7. Junsuk Choe
8. Thomas S. Huang

In Computer Vision and Pattern Recognition

1. Yunchao Wei
2. Guoliang Kang
3. Gengwei Zhang
4. Liyuan Wang
5. Ling Chen
6. Yi Yang
7. Luc Van Gool
8. Jiashi Feng

Citations in the corpus

• SLCA: Slow Learner with Classifier Alignment for Continual Learning on a Pre-trained Model (2023, methodology): “Besides, our results are based on a pre-trained ViT backbone for a fair comparison with previous works [46, 45], without considering other representative architectures such as ResNet and other CL applications on downstream computer vision tasks such as object detection [35], image segmentation [54, ”
• Continual Segmentation with Disentangled Objectness Learning and Class Recognition (2024, unknown): “While the research focus of continual learning is on classification [1, 6, 11, 22, 25, 38, 41, 50, 58], continual segmentation started to thrive recently [3, 5, 7, 12, 32, 36, 40, 51, 57, 60, 61, 65].”