Name
Abstract | ||
|---|---|---|
Learning new concepts from a few of samples is a standard challenge in computer vision. The main directions to improve the learning ability of few-shot training models include (i) a robust similarity learning and (ii) generating or hallucinating additional data from the limited existing samples. In this paper, we follow the latter direction and present a novel data hallucination model. Currently, most datapoint generators contain a specialized network (i.e., GAN) tasked with hallucinating new datapoints, thus requiring large numbers of annotated data for their training in the first place. In this paper, we propose a novel less-costly hallucination method for few-shot learning which utilizes saliency maps. To this end, we employ a saliency network to obtain the foregrounds and backgrounds of available image samples and feed the resulting maps into a two-stream network to hallucinate datapoints directly in the feature space from viable foreground-background combinations. To the best of our knowledge, we are the first to leverage saliency maps for such a task and we demonstrate their usefulness in hallucinating additional datapoints for few-shot learning. Our proposed network achieves the state of the art on publicly available datasets. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/CVPR.2019.00288 | 2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) |
Field | DocType | Volume |
Similarity learning,Feature vector,Pattern recognition,Salience (neuroscience),Computer science,Artificial intelligence,Machine learning,Hallucinating,Hallucinate | Journal | abs/1904.03472 |
ISSN | Citations | PageRank |
1063-6919 | 7 | 0.44 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongguang Zhang | 1 | 106 | 16.70 |
| Jing Zhang | 2 | 24 | 6.36 |
| Piotr Koniusz | 3 | 173 | 16.64 |