IGG: Improved Graph Generation for Domain Adaptive Object Detection

Domain Adaptive Object Detection (DAOD) transfers an object detector from a labeled source domain to a novel unlabeled target domain. Recent works bridge the domain gap by aligning cross-domain pixel-pairs in the non-euclidean graphical space and minimizing the domain discrepancy for adapting semantic distribution. Though great successes, these methods model graphs roughly with coarse semantic sampling due to ignoring the non-informative noises and failing to concentrate on precise semantics alignment. Besides, the coarse graph generation inevitably contains abnormal nodes. These challenges result in biased domain adaptation. Therefore, we propose an Improved Graph Generation (IGG) framework which conducts high-quality graph generation for DAOD. Specifically, we design an Intensive Node Refinement (INR) module that reconstructs the noisy sampled nodes with a memory bank, and contrastively regularizes the noisy features. For better semantics alignment, we decouple the domain-specific style and category-invariant content encoded in graph covariance and selectively eliminate only the domain-specific style. Then, a Precision Graph Optimization (PGO) adaptor is proposed which utilizes the variational inference to down-weight abnormal nodes. Comprehensive experiments on three adaptation benchmarks demonstrate that IGG achieves state-of-the-art results in unsupervised domain adaptation.