Loss Functions in the Era of Semantic Segmentation: A Survey and Outlook

Semantic image segmentation, the process of classifying each pixel in an image into a particular class, plays an important role in many visual understanding systems. As the predominant criterion for evaluating the performance of statistical models, loss functions are crucial for shaping the development of deep learning-based segmentation algorithms and improving their overall performance. To aid researchers in identifying the optimal loss function for their particular application, this survey provides a comprehensive and unified review of $25$ loss functions utilized in image segmentation. We provide a novel taxonomy and thorough review of how these loss functions are customized and leveraged in image segmentation, with a systematic categorization emphasizing their significant features and applications. Furthermore, to evaluate the efficacy of these methods in real-world scenarios, we propose unbiased evaluations of some distinct and renowned loss functions on established medical and natural image datasets. We conclude this review by identifying current challenges and unveiling future research opportunities. Finally, we have compiled the reviewed studies that have open-source implementations on our GitHub page.

MASK4D: Mask Transformer for 4D Panoptic Segmentation

Accurately perceiving and tracking instances over time is essential for the decision-making processes of autonomous agents interacting safely in dynamic environments. With this intention, we propose Mask4D for the challenging task of 4D panoptic segmentation of LiDAR point clouds. Mask4D is the first transformer-based approach unifying semantic instance segmentation and tracking of sparse and irregular sequences of 3D point clouds into a single joint model. Our model directly predicts semantic instances and their temporal associations without relying on any hand-crafted non-learned association strategies such as probabilistic clustering or voting-based center prediction. Instead, Mask4D introduces spatio-temporal instance queries which encode the semantic and geometric properties of each semantic tracklet in the sequence. In an in-depth study, we find that it is critical to promote spatially compact instance predictions as spatio-temporal instance queries tend to merge multiple semantically similar instances, even if they are spatially distant. To this end, we regress 6-DOF bounding box parameters from spatio-temporal instance queries, which is used as an auxiliary task to foster spatially compact predictions. Mask4D achieves a new state-of-the-art on the SemanticKITTI test set with a score of 68.4 LSTQ, improving upon published top-performing methods by at least +4.5%.