Name
Title | ||
|---|---|---|
Few-shot action recognition with implicit temporal alignment and pair similarity optimization |
Abstract | ||
|---|---|---|
Few-shot learning aims to recognize instances from novel classes with few labeled samples, which has great value in research and application. Although there has been a lot of work in this area recently, most of the existing work is based on image classification tasks. Video-based few-shot action recognition has not been explored well and remains challenging: (1) the differences of implementation details among different papers make a fair comparison difficult; (2) the wide variations and misalignment of temporal sequences make the video-level similarity comparison difficult; (3) the scarcity of labeled data makes the optimization difficult. To solve these problems, this paper presents (1) a specific setting to evaluate the performance of few-shot action recognition algorithms; (2) an implicit sequence-alignment algorithm for better video-level similarity comparison; (3) an advanced loss for few-shot learning to optimize pair similarity with limited data. Specifically, we propose a novel few-shot action recognition framework that uses long short-term memory following 3D convolutional layers for sequence modeling and alignment. Circle loss is introduced to maximize the within-class similarity and minimize the between-class similarity flexibly towards a more definite convergence target. Instead of using random or ambiguous experimental settings, we set a concrete criterion analogous to the standard image-based few-shot learning setting for few-shot action recognition evaluation. Extensive experiments on two datasets demonstrate the effectiveness of our proposed method. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.cviu.2021.103250 | Computer Vision and Image Understanding |
Keywords | DocType | Volume |
41A05,41A10,65D05,65D17 | Journal | 210 |
Issue | ISSN | Citations |
1 | 1077-3142 | 2 |
PageRank | References | Authors |
0.37 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Congqi Cao | 1 | 2 | 0.71 |
| Yajuan Li | 2 | 2 | 0.37 |
| Qinyi Lv | 3 | 3 | 2.08 |
| Peng Wang | 4 | 10 | 6.29 |
| Yanning Zhang | 5 | 1613 | 176.32 |