Hierarchical Meta Alignment for cross-domain object detection

Unsupervised domain adaptation (UDA) aims to adapt an object detector from a labeled source domain to an unlabeled target domain. In this task, multiple sub-tasks of different nature are involved, yet existing methods simply sum up the losses and train all the sub-tasks jointly. We, however, find that inconsistent optimization goals between different sub-tasks lead to limited adaptation performance. Specifically, from our analysis, we find notable gradient discrepancies between sub-tasks in a domain adaptive object detector, and especially significant conflicts between domain alignment and detection sub-tasks. Based on this analysis, we propose to solve UDA object detection from a multi-task learning perspective. Specifically, we divide all sub-tasks into two groups, and alleviate both inter-group and intra-group inconsistency via a novel Hierarchical Meta Alignment (HMA) method. At the first level, we construct a Meta Optimization Block (MOB) for each inter-group task pair, which is optimized via the Model-Agnostic Meta-Learning (MAML) algorithm. At the second level, all MOBs are optimized sequentially via the Reptile algorithm. Experimental results on various adaptation scenarios show that our proposed method outperforms previous methods.

Authors

• Yang Li Northwestern Polytechnical University
• Shanshan Zhang Nanjing University of Science and Technology
• Yunan Liu Nanjing University of Science and Technology Dalian Maritime University
• Jian Yang

Keywords

• Domain adaptation
• Object detection
• Hierarchical Meta Alignment (HMA)
• Meta optimization block (MOB)