Diffusion Domain Teacher: Diffusion Guided Domain Adaptive Object Detector (2506.04211v1)

Abstract: Object detectors often suffer a decrease in performance due to the large domain gap between the training data (source domain) and real-world data (target domain). Diffusion-based generative models have shown remarkable abilities in generating high-quality and diverse images, suggesting their potential for extracting valuable feature from various domains. To effectively leverage the cross-domain feature representation of diffusion models, in this paper, we train a detector with frozen-weight diffusion model on the source domain, then employ it as a teacher model to generate pseudo labels on the unlabeled target domain, which are used to guide the supervised learning of the student model on the target domain. We refer to this approach as Diffusion Domain Teacher (DDT). By employing this straightforward yet potent framework, we significantly improve cross-domain object detection performance without compromising the inference speed. Our method achieves an average mAP improvement of 21.2% compared to the baseline on 6 datasets from three common cross-domain detection benchmarks (Cross-Camera, Syn2Real, Real2Artistic}, surpassing the current state-of-the-art (SOTA) methods by an average of 5.7% mAP. Furthermore, extensive experiments demonstrate that our method consistently brings improvements even in more powerful and complex models, highlighting broadly applicable and effective domain adaptation capability of our DDT. The code is available at https://github.com/heboyong/Diffusion-Domain-Teacher.

• The paper introduces a frozen diffusion backbone to extract domain-invariant features, yielding a 21.2% mAP improvement across six datasets.
• It employs a self-training strategy with pseudo labels and an EMA process, enabling effective adaptation between varied domains.
• Results on benchmarks including Cross-Camera, Syn2Real, and Real2Artistic demonstrate a 5.7% mAP advantage over state-of-the-art methods.

Diffusion Domain Teacher: Enhancing Cross-Domain Object Detection

The paper "Diffusion Domain Teacher: Diffusion Guided Domain Adaptive Object Detector" addresses the persistent challenge of domain shift in object detection, where the model's performance typically deteriorates when transitioning from a source domain to a disparate target domain. The authors propose an innovative approach leveraging diffusion-based generative models known for their prowess in generating high-quality and diverse imagery. This method is named the Diffusion Domain Teacher (DDT), which integrates a diffusion model as a robust feature extractor to mitigate domain discrepancies and boost cross-domain detection performance.

Methodology Overview

The central thesis of this paper revolves around using diffusion models for domain adaptation in object detection. The process is as follows:

1. Frozen Diffusion Backbone: The authors utilize a frozen-weight diffusion model during the feature extraction phase. Unlike typical fully-trainable backbones, the diffusion model operates in a distinctive manner, capturing intermediate features during its inversion process—specifically within the U-Net architecture's upsampling layer—which are then adapted for object detection tasks. This mechanism aims to capitalize on the diffusion model's generalized feature representation capabilities across disparate domains.
2. Self-Training with Diffusion Teacher: The DDT framework employs a self-training strategy wherein the diffusion-guided detector is used as a "teacher" to generate pseudo labels for the unlabeled target domain. These pseudo labels are subsequently employed to supervise the student model's training on the target domain. Furthermore, the student model refines the teacher model's weights using an Exponential Moving Average (EMA) process, thereby augmenting model stability and facilitating cross-domain learning.
3. Performance Metrics and Gains: The DDT framework claims a significant edge by reporting an average improvement of 21.2% mAP across six datasets spanning three major benchmarks: Cross-Camera, Syn2Real, and Real2Artistic. This surpasses existing state-of-the-art benchmarks by 5.7% mAP. Such results underscore the efficacy of utilizing diffusion models as a backbone for extracting domain-invariant features.

Implications and Future Directions

The implications of this research are twofold. Practically, implementing diffusion models in adaptive object detection frameworks offers a robust method to tackle domain shift challenges, particularly in real-world applications like autonomous driving and robotic vision. Theoretically, this research opens up new avenues for employing generative models like diffusion models in conventional supervised learning tasks, presenting potential modifications in architecture and computation strategies to enhance training efficiency and inference speed.

While the utilization of frozen-weight diffusion models demonstrates improved cross-domain performance, it highlights limitations concerning intra-domain performance when compared with fully-trainable networks. This insight may drive future research towards hybrid models that incorporate trainable components within diffusion structures or towards optimizing the diffusion inversion process for speed and image fidelity, potentially improving their adaptability in object detection scenarios.

In conclusion, the paper provides a compelling narrative supporting diffusion models' role within self-training frameworks in addressing domain adaptation challenges. The significant numerical improvements and broad applicability suggest promising future trajectories in artificial intelligence research, particularly in refining methods that bridge the gap between generative model strengths and supervised learning needs.