Progressive Attention Networks for Visual Attribute Prediction
This work addresses visual attribute prediction for computer vision applications, but it is incremental as it builds on existing attention mechanisms with specific improvements.
The authors tackled the problem of accurately attending to target objects of varying scales and shapes in images by proposing a progressive attention model that suppresses irrelevant regions across multiple CNN layers, and it outperformed traditional attention methods in visual attribute prediction tasks.
We propose a novel attention model that can accurately attends to target objects of various scales and shapes in images. The model is trained to gradually suppress irrelevant regions in an input image via a progressive attentive process over multiple layers of a convolutional neural network. The attentive process in each layer determines whether to pass or block features at certain spatial locations for use in the subsequent layers. The proposed progressive attention mechanism works well especially when combined with hard attention. We further employ local contexts to incorporate neighborhood features of each location and estimate a better attention probability map. The experiments on synthetic and real datasets show that the proposed attention networks outperform traditional attention methods in visual attribute prediction tasks.