PM-DETR: Domain Adaptive Prompt Memory for Object Detection with Transformers

TL;DR

This paper introduces PM-DETR, a novel domain adaptive method for object detection with transformers that uses prompt memory to better handle distribution shifts across different domains, outperforming existing techniques.

Contribution

The paper proposes a hierarchical Prompt Domain Memory and Prompt Memory Alignment to improve domain adaptation in detection transformers, leveraging domain-specific knowledge and long-term memory.

Findings

Outperforms state-of-the-art methods on three benchmarks

Effectively reduces domain discrepancy with prompt memory techniques

Enhances detection performance across scene, synthetic, and weather variations

Abstract

The Transformer-based detectors (i.e., DETR) have demonstrated impressive performance on end-to-end object detection. However, transferring DETR to different data distributions may lead to a significant performance degradation. Existing adaptation techniques focus on model-based approaches, which aim to leverage feature alignment to narrow the distribution shift between different domains. In this study, we propose a hierarchical Prompt Domain Memory (PDM) for adapting detection transformers to different distributions. PDM comprehensively leverages the prompt memory to extract domain-specific knowledge and explicitly constructs a long-term memory space for the data distribution, which represents better domain diversity compared to existing methods. Specifically, each prompt and its corresponding distribution value are paired in the memory space, and we inject top M distribution-similar prompts into the input and multi-level embeddings of DETR. Additionally, we introduce the Prompt Memory Alignment (PMA) to reduce the discrepancy between the source and target domains by fully leveraging the domain-specific knowledge extracted from the prompt domain memory. Extensive experiments demonstrate that our method outperforms state-of-the-art domain adaptive object detection methods on three benchmarks, including scene, synthetic to real, and weather adaptation. Codes will be released.