| ■論文No. |
CMN24025 |
| ■ページ数 |
5ページ |
■発行日
|
2024/03/25 |
| ■タイトル |
On Out-of-Domain Generalization in Semi-Supervised Pedestrian Attribute Recognition |
| ■タイトル(英語) |
On Out-of-Domain Generalization in Semi-Supervised Pedestrian Attribute Recognition |
| ■著者名 |
Ravikiran Manikandan(Hitachi India Pvt Ltd),Kumar Sharath(Hitachi India Pvt Ltd),Ganesh Ananth(Hitachi India Pvt Ltd) |
| ■著者名(英語) |
Manikandan Ravikiran(Hitachi India Pvt Ltd),Sharath Kumar(Hitachi India Pvt Ltd),Ananth Ganesh(Hitachi India Pvt Ltd) |
| ■価格 |
会員 ¥440 一般 ¥660 |
| ■書籍種類 |
研究会(論文単位) |
| ■グループ名 |
【C】電子・情報・システム部門 通信研究会 |
| ■本誌 |
2024年3月28日-2024年3月29日通信研究会
|
| ■本誌掲載ページ |
43-47ページ |
| ■原稿種別 |
英語 |
| ■電子版へのリンク |
|
| ■キーワード |
Deep Learning, |Pedestrian Attribute Recognition|Semi-supervised Learning|Deep Learning, |Pedestrian Attribute Recognition|Semi-supervised Learning |
| ■要約(日本語) |
Semi Supervised Pedestrian attribute recognition (Semi-PAR) focuses on identification of attributes of people given their input image, with less annotated samples. Recent works have shown Hierarchical Groupwise Temporal Ensemble (Hi-GOTE) based Semi-PAR improve performance over other methods by training groupwise encoders, however there is currently lack of comprehensive evaluation on out-of-domain datasets. Accordingly in this paper we empirically answer (a) How does Hi-GOTE fare against strong out-of-domain data (b) How effective Hi-GOTE is on coarse grained and fine-grained attributes (c) How does Hi-GOTE's generalization performance vary with addition of labelled samples from out-of-domain domain dataset. Empirical results against a novel out-of-domain dataset Hi-ODATA reveals that (a) Hi-GOTE's performance is on par for strong out-of-domain data (b) it performs well against coarse grained attributes and (c) addition of out-of-domain samples improves performance of Hi-GOTE by additional ~2% in accuracy. |
| ■要約(英語) |
Semi Supervised Pedestrian attribute recognition (Semi-PAR) focuses on identification of attributes of people given their input image, with less annotated samples. Recent works have shown Hierarchical Groupwise Temporal Ensemble (Hi-GOTE) based Semi-PAR improve performance over other methods by training groupwise encoders, however there is currently lack of comprehensive evaluation on out-of-domain datasets. Accordingly in this paper we empirically answer (a) How does Hi-GOTE fare against strong out-of-domain data (b) How effective Hi-GOTE is on coarse grained and fine-grained attributes (c) How does Hi-GOTE's generalization performance vary with addition of labelled samples from out-of-domain domain dataset. Empirical results against a novel out-of-domain dataset Hi-ODATA reveals that (a) Hi-GOTE's performance is on par for strong out-of-domain data (b) it performs well against coarse grained attributes and (c) addition of out-of-domain samples improves performance of Hi-GOTE by additional ~2% in accuracy. |
| ■版 型 |
A4 |
