HotShot: A Loss-Guided Data Augmentation and Curriculum Learning Technique for the Task of Semantic Segmentation

Semantic segmentation is an important computer vision task that requires costly pixel-level annotations to train deep neural networks (DNNs) for. Especially for applications like autonomous driving, precise pixel-level understanding of scenes is a decisive factor between success and failure of the application. It follows that every labeled sample of an existing dataset is highly valuable and should be optimally used during training to maximize its value. This is achieved using (1) augmentation of the same labeled sample to help the model learn it in different ways, and (2) curriculum learning to introduce training samples to the model in an strategic order to ease the learning process. In this work, we present HotShot, a loss-guided cropping technique that assesses the DNN's prediction capability during the training to derive probability scores of potential cropping regions. This effectively combines augmentation and curriculum learning in one technique, where a single sample is cropped (augmentation) in regions selected based on the DNN's loss throughout the training (curriculum learning). For UperNet using a ConvNeXt-tiny backbone and DeepLabV3+ architecture using a ResNet-50 backbone, applying HotShot provides a +0.41 p.p. and +0.43 p.p. mIoU improvement over randomly cropping regions on the CityScapes and BDD100K datasets respectively. More interestingly, the analysis shows HotShot primarily boosts the classes that are most challenging for the model. For example, the rider and motorcycle classes on the BDD100K dataset improve by 163% and 129% using DeepLabV3+ with a ResNet-50 backbone. HotShot achieves improved mIoU in almost all cases and normalizes imbalances in learning challenging classes in datasets.