Name
Abstract | ||
|---|---|---|
•We propose discrepant multiple instance learning (D-MIL), and target at enforcing weakly supervised object detection by localizing complementary instances with maximum completeness and minimum redundancy.•We propose learner discrepancy and learner collaboration modules, and formulate a new “teachers-students” model with detection condence back for object localization.•We achieve new state-of-the-art performance for weakly supervised object detection on MS-COCO dataset. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.patcog.2021.108233 | Pattern Recognition |
Keywords | DocType | Volume |
Weakly supervised detection,Multiple instance learning,Learner discrepancy,Collaborative learning | Journal | 122 |
Issue | ISSN | Citations |
1 | 0031-3203 | 0 |
PageRank | References | Authors |
0.34 | 18 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| W. Gao | 1 | 193 | 33.48 |
| Fang Wan | 2 | 21 | 3.44 |
| Jun Yue | 3 | 0 | 0.34 |
| Songcen Xu | 4 | 92 | 8.91 |
| Qixiang Ye | 5 | 913 | 64.51 |