Name
Abstract | ||
|---|---|---|
The advances made in predicting visual saliency using deep neural networks come at the expense of collecting large-scale annotated data. However, pixel-wise annotation is labor-intensive and overwhelming. In this paper, we propose to learn saliency prediction from a single noisy labelling, which is easy to obtain (e.g., from imperfect human annotation or from unsupervised saliency prediction metho... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TPAMI.2020.3046486 | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Keywords | DocType | Volume |
Noise measurement,Labeling,Predictive models,Annotations,Training,Task analysis,Saliency detection | Journal | 43 |
Issue | ISSN | Citations |
8 | 0162-8828 | 0 |
PageRank | References | Authors |
0.34 | 7 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing Zhang | 1 | 24 | 6.36 |
| Yuchao Dai | 2 | 418 | 42.03 |
| Zhang, Tong | 3 | 7126 | 611.43 |
| Mehrtash Tafazzoli Harandi | 4 | 618 | 39.19 |
| Nick Barnes | 5 | 577 | 68.68 |
| Richard I. Hartley | 6 | 9809 | 986.81 |