[R] Our source codes for attention-based dropout layer for weakly supervised object localization (CVPR 2019 Oral)
: We are glad to share our Tensorflow and Pytorch implementations for “Attention-based Dropout Layer for Weakly Supervised Object Localization. CVPR 2019.” Although this repository is still in progress, we believe that the current version can also help researchers and practitioners in this field. More detailed instructions will be released soon. Contributions and stars are welcome!
Weakly Supervised Object Localization (WSOL) techniques learn the object location only using image-level labels, without location annotations. A common limitation for these techniques is that they cover only the most discriminative part of the object, not the entire object. To address this problem, we propose an Attention-based Dropout Layer (ADL), which utilizes the self-attention mechanism to process the feature maps of the model. The proposed method is composed of two key components: 1) hiding the most discriminative part from the model for capturing the integral extent of object, and 2) highlighting the informative region for improving the recognition power of the model. Based on extensive experiments, we demonstrate that the proposed method is effective to improve the accuracy of WSOL, achieving a new state-of-the-art localization accuracy in CUB-200-2011 dataset. We also show that the proposed method is much more efficient in terms of both parameter and computation overheads than existing techniques.