Attention-based Class-Conditioned Alignment for Multi-Source Domain Adaptation of Object Detectors

• URL: http://arxiv.org/abs/2403.09918v4
• Date: Tue, 05 Nov 2024 15:37:00 GMT
• Title: Attention-based Class-Conditioned Alignment for Multi-Source Domain Adaptation of Object Detectors
• Authors: Atif Belal, Akhil Meethal, Francisco Perdigon Romero, Marco Pedersoli, Eric Granger,
• Abstract summary: Domain adaptation methods for object detection (OD) strive to mitigate the impact of distribution shifts by promoting feature alignment across source and target domains. Most state-of-the-art MSDA methods for OD perform feature alignment in a class-agnostic manner. We propose an attention-based class-conditioned alignment method for MSDA that aligns instances of each object category across domains.
• Score: 11.616494893839757
• License:
• Abstract: Domain adaptation methods for object detection (OD) strive to mitigate the impact of distribution shifts by promoting feature alignment across source and target domains. Multi-source domain adaptation (MSDA) allows leveraging multiple annotated source datasets and unlabeled target data to improve the accuracy and robustness of the detection model. Most state-of-the-art MSDA methods for OD perform feature alignment in a class-agnostic manner. This is challenging since the objects have unique modal information due to variations in object appearance across domains. A recent prototype-based approach proposed a class-wise alignment, yet it suffers from error accumulation due to noisy pseudo-labels that can negatively affect adaptation with imbalanced data. To overcome these limitations, we propose an attention-based class-conditioned alignment method for MSDA that aligns instances of each object category across domains. In particular, an attention module coupled with an adversarial domain classifier allows learning domain-invariant and class-specific instance representations. Experimental results on multiple benchmarking MSDA datasets indicate that our method outperforms the state-of-the-art methods and is robust to class imbalance using a conceptually simple class-conditioning method. Our code is available at https://github.com/imatif17/ACIA.

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