Hierarchical deformable part models for heads and tails
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | VISIGRAPP 2018 - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
| Publisher | SCITEPRESS |
| Pages | 45-55 |
| Number of pages | 11 |
| Volume | 5 |
| ISBN (Electronic) | 9789897582905 |
| DOIs | |
| Publication status | Published - 2018 |
| Publication type | A4 Article in a conference publication |
| Event | INTERNATIONAL CONFERENCE ON COMPUTER VISION THEORY AND APPLICATIONS - Duration: 1 Jan 1900 → … |
Conference
| Conference | INTERNATIONAL CONFERENCE ON COMPUTER VISION THEORY AND APPLICATIONS |
|---|---|
| Period | 1/01/00 → … |
Abstract
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.
ASJC Scopus subject areas
Keywords
- Deformable part model, Imbalanced datasets, Localization, Long-tail distribution, Object detection, Sub-category discovery, Visual similarity network