| 講演名 | 2012-11-07 No Bias Left Behind : Covariate Shift Adaptation for Discriminative 3D Pose Estimation , |
|---|---|
| PDFダウンロードページ |  PDFダウンロードページへ |
| 抄録(和) | |
| 抄録(英) | Discriminative, or (structured) prediction, methods have proved effective for variety of problems in computer vision; a notable example is 3D monocular pose estimation. All methods to date, however, relied on an assumption that training (source) and test (target) data come from the same underlying joint distribution. In many real cases, including standard datasets, this assumption is flawed. In presence of training set bias, the learning results in a biased model whose performance degrades on the (target) test set. Under the assumption of covariate shift we propose an unsupervised domain adaptation approach to address this problem. The approach takes the form of training instance re-weighting, where the weights are assigned based on the ratio of training and test marginals evaluated at the samples. Learning with the resulting weighted training samples, alleviates the bias in the learned models. We show the efficacy of our approach by proposing weighted variants of Kernel Regression (KR) and Twin Gaussian Processes (TGP). We show that our weighted variants outperform their un-weighted counterparts and improve on the state-of-the-art performance in the public (HUMANEVA) dataset. |
| キーワード(和) | |
| キーワード(英) | Pose estimation / covariate shift adaptation / RuLSIF / twin Gaussian Processes |
| 資料番号 | IBISML2012-34 |
| 発行日 |
| 研究会情報 | |
| 研究会 | IBISML |
|---|---|
| 開催期間 | 2012/10/31(から1日開催) |
| 開催地（和） | |
| 開催地（英） | |
| テーマ（和） | |
| テーマ（英） | |
| 委員長氏名（和） | |
| 委員長氏名（英） | |
| 副委員長氏名（和） | |
| 副委員長氏名（英） | |
| 幹事氏名（和） | |
| 幹事氏名（英） | |
| 幹事補佐氏名（和） | |
| 幹事補佐氏名（英） | |
| 講演論文情報詳細 | |
| 申込み研究会 | Information-Based Induction Sciences and Machine Learning (IBISML) |
|---|---|
| 本文の言語 | ENG |
| タイトル（和） | |
| サブタイトル（和） | |
| タイトル（英） | No Bias Left Behind : Covariate Shift Adaptation for Discriminative 3D Pose Estimation |
| サブタイトル（和） | |
| キーワード(1)（和/英） | / Pose estimation |
| 第 1 著者 氏名（和/英） | / Makoto YAMADA |
| 第 1 著者 所属（和/英） | NTT Communication Science Laboratories |
| 発表年月日 | 2012-11-07 |
| 資料番号 | IBISML2012-34 |
| 巻番号（vol） | vol.112 |
| 号番号（no） | 279 |
| ページ範囲 | pp.- |
| ページ数 | 8 |
| 発行日 | |