DA-Mamba: Domain Adaptive Hybrid Mamba-Transformer Based One-Stage Object Detection

• URL: http://arxiv.org/abs/2502.11178v2
• Date: Wed, 07 May 2025 10:18:31 GMT
• Title: DA-Mamba: Domain Adaptive Hybrid Mamba-Transformer Based One-Stage Object Detection
• Authors: A. Enes Doruk, Hasan F. Ates,
• Abstract summary: We present the first domain-adaptive Mamba-based one-stage object detection model, DA-Mamba.<n>Inspired by the global modeling and linear complexity of the Mamba architecture, we present the first domain-adaptive Mamba-based one-stage object detection model, DA-Mamba.
• Score: 0.3683202928838613
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent 2D CNN-based domain adaptation approaches struggle with long-range dependencies due to limited receptive fields, making it difficult to adapt to target domains with significant spatial distribution changes. While transformer-based domain adaptation methods better capture distant relationships through self-attention mechanisms that facilitate more effective cross-domain feature alignment, their quadratic computational complexity makes practical deployment challenging for object detection tasks across diverse domains. Inspired by the global modeling and linear computation complexity of the Mamba architecture, we present the first domain-adaptive Mamba-based one-stage object detection model, termed DA-Mamba. Specifically, we combine Mamba's efficient state-space modeling with attention mechanisms to address domain-specific spatial and channel-wise variations. Our design leverages domain-adaptive spatial and channel-wise scanning within the Mamba block to extract highly transferable representations for efficient sequential processing, while cross-attention modules generate long-range, mixed-domain spatial features to enable robust soft alignment across domains. Besides, motivated by the observation that hybrid architectures introduce feature noise in domain adaptation tasks, we propose an entropy-based knowledge distillation framework with margin ReLU, which adaptively refines multi-level representations by suppressing irrelevant activations and aligning uncertainty across source and target domains. Finally, to prevent overfitting caused by the mixed-up features generated through cross-attention mechanisms, we propose entropy-driven gating attention with random perturbations that simultaneously refine target features and enhance model generalization.

Related papers

• Advancing Cross-Organ Domain Generalization with Test-Time Style Transfer and Diversity Enhancement [15.154556569127116]
  We propose a Test-time style transfer (T3s) that uses a bidirectional mapping mechanism to project the features of the source and target domains into a unified feature space.<n>To further increase the style expression space, we introduce a Cross-domain style diversification module.<n>Our method has demonstrated effectiveness on three unseen datasets.
  arXiv  Detail & Related papers  (2025-03-24T11:22:27Z)
• Exploiting Aggregation and Segregation of Representations for Domain Adaptive Human Pose Estimation [50.31351006532924]
  Human pose estimation (HPE) has received increasing attention recently due to its wide application in motion analysis, virtual reality, healthcare, etc.<n>It suffers from the lack of labeled diverse real-world datasets due to the time- and labor-intensive annotation.<n>We introduce a novel framework that capitalizes on both representation aggregation and segregation for domain adaptive human pose estimation.
  arXiv  Detail & Related papers  (2024-12-29T17:59:45Z)
• Multi-Agent Path Finding in Continuous Spaces with Projected Diffusion Models [57.45019514036948]
  Multi-Agent Path Finding (MAPF) is a fundamental problem in robotics.<n>This work proposes a novel approach that integrates constrained optimization with diffusion models for MAPF in continuous spaces.
  arXiv  Detail & Related papers  (2024-12-23T21:27:19Z)
• Feature Based Methods in Domain Adaptation for Object Detection: A Review Paper [0.6437284704257459]
  Domain adaptation aims to enhance the performance of machine learning models when deployed in target domains with distinct data distributions.<n>This review delves into advanced methodologies for domain adaptation, including adversarial learning, discrepancy-based, multi-domain, teacher-student, ensemble, and Vision Language Models.<n>Special attention is given to strategies that minimize the reliance on extensive labeled data, particularly in scenarios involving synthetic-to-real domain shifts.
  arXiv  Detail & Related papers  (2024-12-23T06:34:23Z)
• TransAdapter: Vision Transformer for Feature-Centric Unsupervised Domain Adaptation [0.3277163122167433]
  Unsupervised Domain Adaptation (UDA) aims to utilize labeled data from a source domain to solve tasks in an unlabeled target domain.<n>Traditional CNN-based methods struggle to fully capture complex domain relationships.<n>We propose a novel UDA approach leveraging the Swin Transformer with three key modules.
  arXiv  Detail & Related papers  (2024-12-05T11:11:39Z)
• Improving Anomaly Segmentation with Multi-Granularity Cross-Domain Alignment [17.086123737443714]
  Anomaly segmentation plays a pivotal role in identifying atypical objects in images, crucial for hazard detection in autonomous driving systems. While existing methods demonstrate noteworthy results on synthetic data, they often fail to consider the disparity between synthetic and real-world data domains. We introduce the Multi-Granularity Cross-Domain Alignment framework, tailored to harmonize features across domains at both the scene and individual sample levels.
  arXiv  Detail & Related papers  (2023-08-16T22:54:49Z)
• Dynamic Domain Discrepancy Adjustment for Active Multi-Domain Adaptation [3.367755441623275]
  Multi-source unsupervised domain adaptation (MUDA) aims to transfer knowledge from related source domains to an unlabeled target domain. We propose a novel approach called Dynamic Domain Discrepancy Adjustment for Active Multi-Domain Adaptation (D3AAMDA) This mechanism controls the alignment level of features between each source domain and the target domain, effectively leveraging the local advantageous feature information within the source domains.
  arXiv  Detail & Related papers  (2023-07-26T09:40:19Z)
• Semi-Supervised Domain Adaptation with Auto-Encoder via Simultaneous Learning [18.601226898819476]
  We present a new semi-supervised domain adaptation framework that combines a novel auto-encoder-based domain adaptation model with a simultaneous learning scheme. Our framework holds strong distribution matching property by training both source and target auto-encoders.
  arXiv  Detail & Related papers  (2022-10-18T00:10:11Z)
• Unsupervised Domain Adaptation via Style-Aware Self-intermediate Domain [52.783709712318405]
  Unsupervised domain adaptation (UDA) has attracted considerable attention, which transfers knowledge from a label-rich source domain to a related but unlabeled target domain.<n>We propose a novel style-aware feature fusion method (SAFF) to bridge the large domain gap and transfer knowledge while alleviating the loss of class-discnative information.
  arXiv  Detail & Related papers  (2022-09-05T10:06:03Z)
• Domain Adaptation for Object Detection using SE Adaptors and Center Loss [0.0]
  We introduce an unsupervised domain adaptation method built on the foundation of faster-RCNN to prevent drops in performance due to domain shift. We also introduce a family of adaptation layers that leverage the squeeze excitation mechanism called SE Adaptors to improve domain attention. Finally, we incorporate a center loss in the instance and image level representations to improve the intra-class variance.
  arXiv  Detail & Related papers  (2022-05-25T17:18:31Z)
• Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglement [79.2994130944482]
  We design a Domain Disentanglement Faster-RCNN (DDF) to eliminate the source-specific information in the features for detection task learning. Our DDF method facilitates the feature disentanglement at the global and local stages, with a Global Triplet Disentanglement (GTD) module and an Instance Similarity Disentanglement (ISD) module. By outperforming state-of-the-art methods on four benchmark UDA object detection tasks, our DDF method is demonstrated to be effective with wide applicability.
  arXiv  Detail & Related papers  (2022-01-06T05:43:01Z)
• AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection [90.18752912204778]
  Unsupervised domain adaptation for object detection is a challenging problem with many real-world applications. We propose a novel augmented feature alignment network (AFAN) which integrates intermediate domain image generation and domain-adversarial training. Our approach significantly outperforms the state-of-the-art methods on standard benchmarks for both similar and dissimilar domain adaptations.
  arXiv  Detail & Related papers  (2021-06-10T05:01:20Z)
• Disentanglement-based Cross-Domain Feature Augmentation for Effective Unsupervised Domain Adaptive Person Re-identification [87.72851934197936]
  Unsupervised domain adaptive (UDA) person re-identification (ReID) aims to transfer the knowledge from the labeled source domain to the unlabeled target domain for person matching. One challenge is how to generate target domain samples with reliable labels for training. We propose a Disentanglement-based Cross-Domain Feature Augmentation strategy.
  arXiv  Detail & Related papers  (2021-03-25T15:28:41Z)
• Domain Conditioned Adaptation Network [90.63261870610211]
  We propose a Domain Conditioned Adaptation Network (DCAN) to excite distinct convolutional channels with a domain conditioned channel attention mechanism. This is the first work to explore the domain-wise convolutional channel activation for deep DA networks.
  arXiv  Detail & Related papers  (2020-05-14T04:23:24Z)
• Bi-Directional Generation for Unsupervised Domain Adaptation [61.73001005378002]
  Unsupervised domain adaptation facilitates the unlabeled target domain relying on well-established source domain information. Conventional methods forcefully reducing the domain discrepancy in the latent space will result in the destruction of intrinsic data structure. We propose a Bi-Directional Generation domain adaptation model with consistent classifiers interpolating two intermediate domains to bridge source and target domains.
  arXiv  Detail & Related papers  (2020-02-12T09:45:39Z)
• Contradictory Structure Learning for Semi-supervised Domain Adaptation [67.89665267469053]
  Current adversarial adaptation methods attempt to align the cross-domain features. Two challenges remain unsolved: 1) the conditional distribution mismatch and 2) the bias of the decision boundary towards the source domain. We propose a novel framework for semi-supervised domain adaptation by unifying the learning of opposite structures.
  arXiv  Detail & Related papers  (2020-02-06T22:58:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.