Andrei Sharf

Interpretation of predictions made by Convolutional Neural Networks (CNNs) is a rapidly growing field of research. A common approach involves enhancing semantic segmentation predictions through the generation of heatmaps that illustrate the significance of individual pixels in the segmentation. Nevertheless, the selection of beneficial features from these heatmaps remains a challenge. This is because the introduced information often contains interfering factors such as mutual features between different objects, background, and insufficient heat map resolution which often diminish its effectiveness. To overcome these limitations, we introduce Refined Weak Slices (RWS). Our main idea is to identify low attention regions in heat maps i.e. weak slices, in conjunction with segmentation accuracy, and utilize them to select effective features across different DNN layers, to enhance segmentation. We then seamlessly integrate these features back into the CNN, thus refining and enhancing the semantic segmentation result with selected features. Through extensive experiments, we demonstrate that incorporating the RWS module into state-of-the-art methods yields a notable improvement in the average mIoU by 2.84% on benchmark datasets (VOC 2012, COCOStuff, ADE20K, Cityscapes) for both ResNet-101 and ResNet-50 architectures. Furthermore, we achieve a maximum improvement of 5.8% with a single CNN. Overall, the combination of RWS and CNNs exhibits excellent performance in image segmentation tasks.