Attention to Scale: Scale-aware Semantic Image Segmentation
This work addresses the challenge of effectively incorporating multi-scale features in segmentation models, which is crucial for applications in computer vision, though it is incremental as it builds on existing FCN-based methods.
The paper tackled the problem of improving semantic image segmentation by proposing an attention mechanism that learns to weight multi-scale features at each pixel location, achieving state-of-the-art performance on datasets like PASCAL VOC 2012 and MS-COCO 2014.
Incorporating multi-scale features in fully convolutional neural networks (FCNs) has been a key element to achieving state-of-the-art performance on semantic image segmentation. One common way to extract multi-scale features is to feed multiple resized input images to a shared deep network and then merge the resulting features for pixelwise classification. In this work, we propose an attention mechanism that learns to softly weight the multi-scale features at each pixel location. We adapt a state-of-the-art semantic image segmentation model, which we jointly train with multi-scale input images and the attention model. The proposed attention model not only outperforms average- and max-pooling, but allows us to diagnostically visualize the importance of features at different positions and scales. Moreover, we show that adding extra supervision to the output at each scale is essential to achieving excellent performance when merging multi-scale features. We demonstrate the effectiveness of our model with extensive experiments on three challenging datasets, including PASCAL-Person-Part, PASCAL VOC 2012 and a subset of MS-COCO 2014.