SIGMA: Semantic-complete Graph Matching for Domain Adaptive Object Detection (2203.06398v3)

Abstract: Domain Adaptive Object Detection (DAOD) leverages a labeled domain to learn an object detector generalizing to a novel domain free of annotations. Recent advances align class-conditional distributions by narrowing down cross-domain prototypes (class centers). Though great success,they ignore the significant within-class variance and the domain-mismatched semantics within the training batch, leading to a sub-optimal adaptation. To overcome these challenges, we propose a novel SemantIc-complete Graph MAtching (SIGMA) framework for DAOD, which completes mismatched semantics and reformulates the adaptation with graph matching. Specifically, we design a Graph-embedded Semantic Completion module (GSC) that completes mismatched semantics through generating hallucination graph nodes in missing categories. Then, we establish cross-image graphs to model class-conditional distributions and learn a graph-guided memory bank for better semantic completion in turn. After representing the source and target data as graphs, we reformulate the adaptation as a graph matching problem, i.e., finding well-matched node pairs across graphs to reduce the domain gap, which is solved with a novel Bipartite Graph Matching adaptor (BGM). In a nutshell, we utilize graph nodes to establish semantic-aware node affinity and leverage graph edges as quadratic constraints in a structure-aware matching loss, achieving fine-grained adaptation with a node-to-node graph matching. Extensive experiments verify that SIGMA outperforms existing works significantly. Our code is available at https://github.com/CityU-AIM-Group/SIGMA.

• The paper introduces a novel framework that reformulates domain adaptive object detection as a graph matching problem to bridge semantic gaps.
• It employs a Graph-embedded Semantic Completion module to generate hallucination nodes and a Bipartite Graph Matching adaptor for fine-grained semantic alignment.
• Extensive experiments demonstrate that SIGMA outperforms existing methods on multiple benchmarks, enhancing object detection in diverse real-world scenarios.

Semantic-complete Graph Matching for Domain Adaptive Object Detection

The paper "SIGMA: Semantic-complete Graph Matching for Domain Adaptive Object Detection" introduces a novel framework for improving the performance of Domain Adaptive Object Detection (DAOD) by leveraging graph-based methodologies. The key motivation behind this work is addressing the existing challenges in DAOD, such as significant within-class variance and domain-mismatched semantics, which currently lead to sub-optimal adaptations.

The proposed SIGMA framework offers a transformative approach to DAOD by completing mismatched semantics and reformulating the adaptation problem as a graph matching task. The framework consists of two primary components: a Graph-embedded Semantic Completion (GSC) module and a Bipartite Graph Matching (BGM) adaptor.

Key Contributions

1. Graph-embedded Semantic Completion Module (GSC): This module addresses the problem of mismatched semantics by generating hallucination nodes for missing categories, thus ensuring a semantic-complete node set for both domains. It uses a graph-guided memory bank to facilitate the completion of these semantics and models class-conditional distributions through cross-image graphs.
2. Bipartite Graph Matching Adaptor (BGM): The BGM reformulates domain adaptation as a graph matching problem, focusing on finding well-matched node pairs across graphs to reduce the domain gap. It employs a structure-aware matching loss, utilizing both graph nodes and edges to achieve fine-grained adaptation.

Methodology

The methodology of SIGMA involves several innovative steps. Initially, the Semantic Completion module generates hallucination graph nodes for categories missing in the batch during training. This is followed by establishing cross-image graphs to model class-conditional distributions. Additionally, the graphs enable the learning of a graph-guided memory bank that enhances the semantic completion process. The core adaptation process is then cast as a graph matching problem, which is solved using a Bipartite Graph Matching strategy. Here, graph nodes help create semantic-aware node affinities, and graph edges are employed as quadratic constraints in a structure-aware matching loss function, facilitating node-to-node graph matching.

Results and Implications

The authors report extensive experiments that demonstrate SIGMA’s significant outperformance compared to existing methods on three benchmarks. The numerical results highlight the robustness and efficacy of utilizing graph matching in domain adaptation, particularly in scenarios with variabilities such as different weather conditions or between simulated and real-world images.

Theoretical and Practical Implications

Theoretically, this work extends the application of graph matching theory into the domain of object detection, suggesting a shift from prototype alignment methods to a more distributive and structured approach. Practically, this may enhance real-world applications of object detection systems such as autonomous vehicles or video surveillance, where adaptability to novel scenes or conditions without manual re-annotation is crucial.

Future Directions

Future research could explore more sophisticated graph structures or extend the semantic graph matching methodology to other computer vision tasks that suffer from domain shift. Furthermore, integrating this approach with other modalities like depth information or multi-camera setups could be beneficial.

In conclusion, SIGMA presents a structured graph-theoretical approach to DAOD, providing new insights into fine-grained semantic alignment across domains. This method not only bridges existing gaps but also opens up new possibilities for further explorations within the field of domain adaptation.