Hierarchical deformable part models for heads and tails
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Yksityiskohdat
| Alkuperäiskieli | Englanti |
|---|---|
| Otsikko | VISIGRAPP 2018 - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
| Kustantaja | SCITEPRESS |
| Sivut | 45-55 |
| Sivumäärä | 11 |
| Vuosikerta | 5 |
| ISBN (elektroninen) | 9789897582905 |
| DOI - pysyväislinkit | |
| Tila | Julkaistu - 2018 |
| OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisussa |
| Tapahtuma | INTERNATIONAL CONFERENCE ON COMPUTER VISION THEORY AND APPLICATIONS - Kesto: 1 tammikuuta 1900 → … |
Conference
| Conference | INTERNATIONAL CONFERENCE ON COMPUTER VISION THEORY AND APPLICATIONS |
|---|---|
| Ajanjakso | 1/01/00 → … |
Tiivistelmä
Imbalanced long-tail distributions of visual class examples inhibit accurate visual detection, which is addressed by a novel Hierarchical Deformable Part Model (HDPM). HDPM constructs a sub-category hierarchy by alternating bootstrapping and Visual Similarity Network (VSN) based discovery of head and tail sub-categories. We experimentally evaluate HDPM and compare with other sub-category aware visual detection methods with a moderate size dataset (Pascal VOC 2007), and demonstrate its scalability to a large scale dataset (ILSVRC 2014 Detection Task). The proposed HDPM consistently achieves significant performance improvement in both experiments.