TITAN: Query-Token Based Domain Adaptive Adversarial Learning

We focus on source-free domain adaptive object detection (SF-DAOD) problem, where the model has to adapt to an unlabelled target domain without using source data. Majority of existing frameworks for the problem employ a studentteacher $(S T)$ framework where pseudo-labels are generated via a source-pretrained model for further fine-tuning. We observe that the performance of a student model often degrades drastically, due to the collapse of teacher model, primarily caused by high noise in pseudo-labels, resulting from domain bias, discrepancies, and a significant domain shift across domains. To obtain reliable pseudo-labels, we propose a Target-based Iterative Query-Token Adversarial Network (TITAN) which separates the target images into two subsets that are similar to the source (easy) and those that are dissimilar (hard). We propose a strategy to estimate variance to partition the target domain. This approach leverages the insight that higher detection variances correspond to higher recall and greater similarity to the source domain. Also, we incorporate query-token based adversarial modules into a student-teacher baseline framework to reduce the domain gaps between two feature representations. Experiments conducted on four natural imaging datasets and two challenging medical datasets have substantiated the superior performance of TITAN compared to existing state-of-the-art (SOTA) methodologies. We report an mAP improvement of $+22.7,+22.2,+21.1$, and +3.7 percent over the current SOTA on C2F, C2B, S2C, and K2C benchmarks, respectively. Code is available at https://github.com/Tajamul21/TITAN