Visual Recognition by Learning From Web Data: A Weakly Supervised Domain Generalization Approach

CVPR 2015  ·  Li Niu, Wen Li, Dong Xu ·

In this work, we formulate a new weakly supervised domain generalization problem for the visual recognition task by using loosely labeled web images/videos as training data. Specifically, we aim to address two challenging issues when learning robust classifiers: 1) enhancing the generalization capability of the learnt classifiers to any unseen target domain; and 2) coping with noise in the labels of training web images/videos in the source domain. To address the first issue, we assume the training web images/videos may come from multiple hidden domains with different data distributions. We then extend the multi-class SVM formulation to learn one classifier for each class and each latent domain such that multiple classifiers from each class can be effectively integrated to achieve better generalization capability. To address the second issue, we partition the training samples in each class into multiple clusters. By treating each cluster as a "bag" and the samples in each cluster as "instances", we formulate a new multi-instance learning (MIL) problem for domain generalization by selecting a subset of training samples from each training bag and simultaneously learning the optimal classifiers based on the selected samples. Moreover, we also extend our newly proposed Weakly Supervised Domain Generalization (WSDG) approach by taking advantage of the additional textual descriptions that are only available in the training web images/videos as privileged information. Extensive experiments on four benchmark datasets demonstrate the effectiveness of our new approaches for visual recognition by learning from web data.

PDF Abstract
No code implementations yet. Submit your code now

Datasets


  Add Datasets introduced or used in this paper

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods


No methods listed for this paper. Add relevant methods here