Mining Unseen Classes via Regional Objectness: A Simple Baseline for Incremental Segmentation

By Zekang Zhang, Guangyu Gao, Zhiyuan Fang, Jianbo Jiao, Yunchao Wei. 2022

51 citations recorded by the source corpus.

Incremental or continual learning has been extensively studied for image classification tasks to alleviate catastrophic forgetting, a phenomenon that earlier learned knowledge is forgotten when learning new concepts. For class incremental semantic segmentation, such a phenomenon often becomes much worse due to the background shift, i.e., some concepts learned at previous stages are assigned to the background class at the current training stage, therefore, significantly reducing the performance of these old concepts. To address this issue, we propose a simple yet effective method in this paper, named Mining unseen Classes via Regional Objectness for Segmentation (MicroSeg). Our MicroSeg is based on the assumption that background regions with strong objectness possibly belong to those concepts in the historical or future stages. Therefore, to avoid forgetting old knowledge at the current training stage, our MicroSeg first splits the given image into hundreds of segment proposals with a proposal generator. Those segment proposals with strong objectness from the background are then clustered and assigned newly-defined labels during the optimization. In this way, the distribution characterizes of old concepts in the feature space could be better perceived, relieving the catastrophic forgetting caused by the background shift accordingly. Extensive experiments on Pascal VOC and ADE20K datasets show competitive results with state-of-the-art, well validating the effectiveness of the proposed MicroSeg.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

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. Yao Zhao
3. Guoliang Kang
4. Xiaodan Liang
5. Gengwei Zhang
6. Liyuan Wang
7. Ling Chen
8. Jianbo Jiao

In Computer Vision and Pattern Recognition

1. Yunchao Wei
2. Xiaodan Liang
3. Yao Zhao
4. Guoliang Kang
5. Yi Yang
6. Luc Van Gool
7. Gengwei Zhang
8. Jiashi Feng

In Radiology, Nuclear Medicine and Imaging

1. Liyuan Wang
2. Yao Zhao
3. Xiaodan Liang
4. Yunchao Wei

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, ”
• Incremental Nuclei Segmentation from Histopathological Images via Future-class Awareness and Compatibility-inspired Distillation (2024, methodology): “In order to improve both stability and plasticity, a few studies [5, 39] propose to learn future classes in advance to prepare for learning new classes.”
• CTP: Towards Vision-Language Continual Pretraining via Compatible Momentum Contrast and Topology Preservation (2023, background): “Prior studies on continual learning [28, 80, 65, 78, 76] focused on supervised class-incremental learning (CIL), as Figure 1(a), which aims to maintain discriminative features for known classes and expand new classifiers to learn new classes.”
• 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].”
• CoinSeg: Contrast Inter- and Intra- Class Representations for Incremental Segmentation (2023, methodology): “MicroSeg [49] introduces mask proposals to CISS and further clarifies image labels to tackle background shifts.”