OurDB: Ouroboric Domain Bridging for Multi-Target Domain Adaptive Semantic Segmentation

Multi-target domain adaptation (MTDA) for semantic segmentation poses a significant challenge, as it involves multiple target domains with varying distributions. The goal of MTDA is to minimize the domain discrepancies among a single source and multi-target domains, aiming to train a single model that excels across all target domains. Previous MTDA approaches typically employ multiple teacher architectures, where each teacher specializes in one target domain to simplify the task. However, these architectures hinder the student model from fully assimilating comprehensive knowledge from all target-specific teachers and escalate training costs with increasing target domains. In this paper, we propose an ouroboric domain bridging (OurDB) framework, offering an efficient solution to the MTDA problem using a single teacher architecture. This framework dynamically cycles through multiple target domains, aligning each domain individually to restrain the biased alignment problem, and utilizes Fisher information to minimize the forgetting of knowledge from previous target domains. We also propose a context-guided class-wise mixup (CGMix) that leverages contextual information tailored to diverse target contexts in MTDA. Experimental evaluations conducted on four urban driving datasets (i.e., GTA5, Cityscapes, IDD, and Mapillary) demonstrate the superiority of our method over existing state-of-the-art approaches.

itKD: Interchange Transfer-based Knowledge Distillation for 3D Object Detection

Recently, point-cloud based 3D object detectors have achieved remarkable progress. However, most studies are limited to the development of deep learning architectures for improving only their accuracy. In this paper, we propose an autoencoder-style framework comprising channel-wise compression and decompression via interchange transfer for knowledge distillation. To learn the map-view feature of a teacher network, the features from a teacher and student network are independently passed through the shared autoencoder; here, we use a compressed representation loss that binds the channel-wised compression knowledge from both the networks as a kind of regularization. The decompressed features are transferred in opposite directions to reduce the gap in the interchange reconstructions. Lastly, we present an attentive head loss for matching the pivotal detection information drawn by the multi-head self-attention mechanism. Through extensive experiments, we verify that our method can learn the lightweight model that is well-aligned with the 3D point cloud detection task and we demonstrate its superiority using the well-known public datasets Waymo and nuScenes.