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[DTC19] Exploiting Negative Evidence for Deep Latent Structured Models
Revue Internationale avec comité de lecture : Journal IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41(2), pp. 337-351, 2019, (doi: 10.1109/TPAMI.2017.2788435)Mots clés: Weakly Supervised Learning, Deep Learning, Structured Prediction
Résumé:
The abundance of image-level labels and the lack of large scale detailed annotations (e.g. bounding boxes, segmentation masks) promotes the development of weakly supervised learning (WSL) models. In this work, we propose a novel framework for WSL of deep convolutional neural networks dedicated to learn localized features from global image-level annotations. The core of the approach is a new latent structured output model equipped with a pooling function which explicitly models negative evidence, e.g. a cow detector should strongly penalize the prediction of the bedroom class. We show that our model can be trained end-to-end for different visual recognition tasks: multi-class and multi-label classification, and also structured average precision (AP) ranking. Extensive experiments highlight the relevance of the proposed method: our model outperforms state-of-the art results on six datasets. We also show that our framework can be used to improve the performance of state-of-the-art deep models for large scale image classification on ImageNet. Finally, we evaluate our model for weakly supervised tasks: in particular, a direct adaptation for weakly supervised segmentation provides a very competitive model.
BibTeX
| @article { | |||
| DTC19, | |||
| title | = | "{Exploiting Negative Evidence for Deep Latent Structured Models}", | |
| author | = | "T. Durand and N. Thome and M. Cord", | |
| journal | = | "IEEE Transactions on Pattern Analysis and Machine Intelligence", | |
| year | = | 2019, | |
| volume | = | 41, | |
| number | = | 2, | |
| pages | = | "337-351", | |
| doi | = | " 10.1109/TPAMI.2017.2788435", | |
| } | |||
