Exploring Object Relation in Mean Teacher for Cross-Domain Detection (1904.11245v2)

Abstract: Rendering synthetic data (e.g., 3D CAD-rendered images) to generate annotations for learning deep models in vision tasks has attracted increasing attention in recent years. However, simply applying the models learnt on synthetic images may lead to high generalization error on real images due to domain shift. To address this issue, recent progress in cross-domain recognition has featured the Mean Teacher, which directly simulates unsupervised domain adaptation as semi-supervised learning. The domain gap is thus naturally bridged with consistency regularization in a teacher-student scheme. In this work, we advance this Mean Teacher paradigm to be applicable for cross-domain detection. Specifically, we present Mean Teacher with Object Relations (MTOR) that novelly remolds Mean Teacher under the backbone of Faster R-CNN by integrating the object relations into the measure of consistency cost between teacher and student modules. Technically, MTOR firstly learns relational graphs that capture similarities between pairs of regions for teacher and student respectively. The whole architecture is then optimized with three consistency regularizations: 1) region-level consistency to align the region-level predictions between teacher and student, 2) inter-graph consistency for matching the graph structures between teacher and student, and 3) intra-graph consistency to enhance the similarity between regions of same class within the graph of student. Extensive experiments are conducted on the transfers across Cityscapes, Foggy Cityscapes, and SIM10k, and superior results are reported when comparing to state-of-the-art approaches. More remarkably, we obtain a new record of single model: 22.8% of mAP on Syn2Real detection dataset.

• The paper introduces MTOR, extending the Mean Teacher model by enforcing region-level consistency to mitigate object instance variations across domains.
• It incorporates inter-graph and intra-graph consistencies via object relational graphs within a Faster R-CNN framework to enhance detection accuracy.
• Extensive experiments, including a SYNTHIA to Cityscapes transfer achieving 22.8% mAP, validate MTOR's state-of-the-art performance.

Exploring Object Relation in Mean Teacher for Cross-Domain Detection

This essay reviews the paper "Exploring Object Relation in Mean Teacher for Cross-Domain Detection," which presents an innovative approach for improving domain adaptation in object detection tasks. The authors propose a sophisticated methodology that builds upon the Mean Teacher model, leveraging region-level consistency and graph-structured consistency to address domain shift challenges.

Framework Overview

The core of the paper revolves around adapting the Mean Teacher paradigm, traditionally used in semi-supervised learning, to the task of cross-domain object detection. The proposed model, named Mean Teacher with Object Relations (MTOR), incorporates object relational graphs into a standard Faster R-CNN framework. This integration enables the model to capture and utilize relationships between object regions, thereby improving cross-domain detection performance.

Key Contributions

1. Region-Level Consistency: The paper extends the Mean Teacher approach from image-level to region-level consistency. This extension is critical for object detection, where precise localization and classification of object regions are essential. By enforcing consistency at the region level, the model implicitly reduces local instance variations, such as scale and color jitter.
2. Graph-Structured Consistency: MTOR introduces two additional consistency measures based on relational graphs: inter-graph and intra-graph consistency.
   • Inter-Graph Consistency ensures that the structure of object relations remains consistent between teacher and student models, even under perturbations.
   • Intra-Graph Consistency enhances similarity between regions of the same class within the student model's graph, aiming to reduce intra-class variation.
3. Quantitative Results: Extensive experiments across several benchmark datasets demonstrate the efficacy of MTOR. Notably, in the challenging SYNTHIA to Cityscapes transfer, MTOR outperforms existing methods, achieving a mean Average Precision (mAP) of 22.8%—setting a new performance record for single models on the Syn2Real detection dataset.

Implications and Future Directions

The implications of this research are twofold. Practically, MTOR provides a robust framework for deploying object detection models trained on synthetic data to real-world applications, minimizing the labeling costs associated with annotating large datasets. Theoretically, the introduction of graph-structured consistency represents a novel approach to incorporate relational information in unsupervised domain adaptation, a concept that could be explored further in other computer vision tasks.

Future research could extend this work by exploring different graph construction strategies or incorporating more sophisticated graph neural networks to enhance relational reasoning. Additionally, investigating the scalability of the proposed method to handle larger datasets with more complex scenes could be another promising direction. Integrating the MTOR framework with real-time processing capabilities would also be beneficial for applications requiring fast and accurate object detection.

In summary, the paper makes a significant contribution to the field of cross-domain object detection. By innovatively adapting the Mean Teacher model to factor in object relations, the authors open new avenues for research and application in domain adaptation.