ARNet: Active-Reference Network for Few-Shot Image Semantic Segmentation

By Guangchen Shi, Yirui Wu, Palaiahnakote Shivakumara, Umapada Pal, Tong Lu. 2021

7 citations recorded by the source corpus.

To make predictions on unseen classes, few-shot segmentation becomes a research focus recently. However, most methods build on pixel-level annotation requiring quantity of manual work. Moreover, inherent information on same-category objects to guide segmentation could have large diversity in feature representation due to differences in size, appearance, layout, and so on. To tackle these problems, we present an active-reference network (ARNet) for few-shot segmentation. The proposed active-reference mechanism not only supports accurately cooccurrent objects in either support or query images, but also relaxes high constraint on pixel-level labeling, allowing for weakly boundary labeling. To extract more intrinsic feature representation, a category-modulation module (CMM) is further applied to fuse features extracted from multiple support images, thus forgetting useless and enhancing contributive information. Experiments on PASCAL-5i dataset show the proposed method achieves a m-IOU score of 56.5% for 1-shot and 59.8% for 5-shot segmentation, being 0.5% and 1.3% higher than current state-of-the-art method.

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. Wenhai Wang
2. Tong Lu
3. Yirui Wu
4. Guangchen Shi
5. J. Liu
6. Shaohua Wan

In Computer Vision and Pattern Recognition

1. Tong Lu
2. Wenhai Wang
3. Yirui Wu
4. Guangchen Shi
5. J. Liu
6. Shaohua Wan
7. Luc Van Gool
8. Yu Qiao

Citations in the corpus

• Incremental Few-Shot Semantic Segmentation via Embedding Adaptive-Update and Hyper-class Representation (2022, methodology): “Few-shot semantic segmentation [21, 28, 40] addresses to segment a new category of images with few samples, decreasing the cost of expensive pixel-level annotations.”