2021 IEEE International Conference on Acoustics, Speech and Signal Processing
6-11 June 2021 • Toronto, Ontario, Canada
Extracting Knowledge from Information
| Paper ID | IVMSP-9.2 | ||
| Paper Title | REPRESENTATIVE LOCAL FEATURE MINING FOR FEW-SHOT LEARNING | ||
| Authors | Kun Yan, Peking University, China; Lingbo Liu, Sun Yat-Sen University, China; Jun Hou, Sensetime, China; Ping Wang, Peking University, China | ||
| Session | IVMSP-9: Zero and Few Short Learning | ||
| Location | Gather.Town | ||
| Session Time: | Wednesday, 09 June, 13:00 - 13:45 | ||
| Presentation Time: | Wednesday, 09 June, 13:00 - 13:45 | ||
| Presentation | Poster | ||
| Topic | Image, Video, and Multidimensional Signal Processing: [IVTEC] Image & Video Processing Techniques | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | Few-shot learning aims to recognize unseen images of new classes with only a few training examples. While great progress has been made with deep learning technology, most metric-based works rely on the measurement based on global feature representation of images, which is sensitive to background factors due to the scarcity of training data. Given this, we propose a novel method that chooses representative local features to facilitate few-shot learning. Specifically, we propose a “task-specific guided” strategy to mine local features that are task-specific and discriminative. For each task, we first mine representative local features for labeled images by a loss guided mechanism. Then these local features are used to guide a classifier to mine representative local features for unlabeled images. In this way, task-specific representative local features can be selected for better classification. We empirically show our method can effectively alleviate the negative effect introduced by background factors. Extensive experiments on two few-shot benchmarks show the effectiveness of the proposed method. | ||