Dual-attention Guided Dropblock Module for Weakly Supervised Object Localization
arXiv:2003.04719
Abstract
Attention mechanisms is frequently used to learn the discriminative features for better feature representations. In this paper, we extend the attention mechanism to the task of weakly supervised object localization (WSOL) and propose the dual-attention guided dropblock module (DGDM), which aims at learning the informative and complementary visual patterns for WSOL. This module contains two key components, the channel attention guided dropout (CAGD) and the spatial attention guided dropblock (SAGD). To model channel interdependencies, the CAGD ranks the channel attentions and treats the top-k attentions with the largest magnitudes as the important ones. It also keeps some low-valued elements to increase their value if they become important during training. The SAGD can efficiently remove the most discriminative information by erasing the contiguous regions of feature maps rather than individual pixels. This guides the model to capture the less discriminative parts for classification. Furthermore, it can also distinguish the foreground objects from the background regions to alleviate the attention misdirection. Experimental results demonstrate that the proposed method achieves new state-of-the-art localization performance.
arXiv commentAccepted by the 25th International Conference on Pattern Recognition (ICPR 2020)
References in corpus (9)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Improving neural networks by preventing co-adaptation of feature detectors
- Improved Regularization of Convolutional Neural Networks with Cutout
- Recurrent Models of Visual Attention
- DropBlock: A regularization method for convolutional networks
- Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning
- The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification
- OSLNet: Deep Small-Sample Classification with an Orthogonal Softmax Layer
- Survey of Dropout Methods for Deep Neural Networks