Exploiting Domain Transferability for Collaborative Inter-level Domain Adaptive Object Detection

TL;DR

This paper introduces a novel domain adaptive object detection framework that leverages inter-level relations and transferable information to improve detection accuracy across domains, achieving state-of-the-art results.

Contribution

The paper proposes a new framework with three modules that exploit inter-level relations and transferable features for improved domain adaptive object detection.

Findings

Modules improve detection performance synergistically.

Achieves state-of-the-art results on multiple benchmarks.

Reduces negative transfer during training.

Abstract

Domain adaptation for object detection (DAOD) has recently drawn much attention owing to its capability of detecting target objects without any annotations. To tackle the problem, previous works focus on aligning features extracted from partial levels (e.g., image-level, instance-level, RPN-level) in a two-stage detector via adversarial training. However, individual levels in the object detection pipeline are closely related to each other and this inter-level relation is unconsidered yet. To this end, we introduce a novel framework for DAOD with three proposed components: Multi-scale-aware Uncertainty Attention (MUA), Transferable Region Proposal Network (TRPN), and Dynamic Instance Sampling (DIS). With these modules, we seek to reduce the negative transfer effect during training while maximizing transferability as well as discriminability in both domains. Finally, our framework implicitly learns domain invariant regions for object detection via exploiting the transferable information and enhances the complementarity between different detection levels by collaboratively utilizing their domain information. Through ablation studies and experiments, we show that the proposed modules contribute to the performance improvement in a synergic way, demonstrating the effectiveness of our method. Moreover, our model achieves a new state-of-the-art performance on various benchmarks.