DSD-DA: Distillation-based Source Debiasing for Domain Adaptive Object Detection

• URL: http://arxiv.org/abs/2311.10437v2
• Date: Fri, 17 May 2024 09:36:10 GMT
• Title: DSD-DA: Distillation-based Source Debiasing for Domain Adaptive Object Detection
• Authors: Yongchao Feng, Shiwei Li, Yingjie Gao, Ziyue Huang, Yanan Zhang, Qingjie Liu, Yunhong Wang,
• Abstract summary: We propose a novel Distillation-based Source Debiasing (DSD) framework for Domain Adaptive Object Detection (DAOD) This framework distills domain-agnostic knowledge from a pre-trained teacher model, improving the detector's performance on both domains. We also present a Domain-aware Consistency Enhancing (DCE) strategy, in which these information are formulated into a new localization representation.
• Score: 37.01880023537362
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Though feature-alignment based Domain Adaptive Object Detection (DAOD) methods have achieved remarkable progress, they ignore the source bias issue, i.e., the detector tends to acquire more source-specific knowledge, impeding its generalization capabilities in the target domain. Furthermore, these methods face a more formidable challenge in achieving consistent classification and localization in the target domain compared to the source domain. To overcome these challenges, we propose a novel Distillation-based Source Debiasing (DSD) framework for DAOD, which can distill domain-agnostic knowledge from a pre-trained teacher model, improving the detector's performance on both domains. In addition, we design a Target-Relevant Object Localization Network (TROLN), which can mine target-related localization information from source and target-style mixed data. Accordingly, we present a Domain-aware Consistency Enhancing (DCE) strategy, in which these information are formulated into a new localization representation to further refine classification scores in the testing stage, achieving a harmonization between classification and localization. Extensive experiments have been conducted to manifest the effectiveness of this method, which consistently improves the strong baseline by large margins, outperforming existing alignment-based works.

Related papers

• Open-Set Domain Adaptation with Visual-Language Foundation Models [51.49854335102149]
  Unsupervised domain adaptation (UDA) has proven to be very effective in transferring knowledge from a source domain to a target domain with unlabeled data. Open-set domain adaptation (ODA) has emerged as a potential solution to identify these classes during the training phase.
  arXiv  Detail & Related papers  (2023-07-30T11:38:46Z)
• AVATAR: Adversarial self-superVised domain Adaptation network for TARget domain [11.764601181046496]
  This paper presents an unsupervised domain adaptation (UDA) method for predicting unlabeled target domain data. We propose the Adversarial self-superVised domain Adaptation network for the TARget domain (AVATAR) algorithm. Our proposed model significantly outperforms state-of-the-art methods on three UDA benchmarks.
  arXiv  Detail & Related papers  (2023-04-28T20:31:56Z)
• Continual Source-Free Unsupervised Domain Adaptation [37.060694803551534]
  Existing Source-free Unsupervised Domain Adaptation approaches exhibit catastrophic forgetting. We propose a Continual SUDA (C-SUDA) framework to cope with the challenge of SUDA in a continual learning setting.
  arXiv  Detail & Related papers  (2023-04-14T20:11:05Z)
• Self-training through Classifier Disagreement for Cross-Domain Opinion Target Extraction [62.41511766918932]
  Opinion target extraction (OTE) or aspect extraction (AE) is a fundamental task in opinion mining. Recent work focus on cross-domain OTE, which is typically encountered in real-world scenarios. We propose a new SSL approach that opts for selecting target samples whose model output from a domain-specific teacher and student network disagrees on the unlabelled target data.
  arXiv  Detail & Related papers  (2023-02-28T16:31:17Z)
• Source-Free Domain Adaptation via Distribution Estimation [106.48277721860036]
  Domain Adaptation aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain whose data distributions are different. Recently, Source-Free Domain Adaptation (SFDA) has drawn much attention, which tries to tackle domain adaptation problem without using source data. In this work, we propose a novel framework called SFDA-DE to address SFDA task via source Distribution Estimation.
  arXiv  Detail & Related papers  (2022-04-24T12:22:19Z)
• Instance Relation Graph Guided Source-Free Domain Adaptive Object Detection [79.89082006155135]
  Unsupervised Domain Adaptation (UDA) is an effective approach to tackle the issue of domain shift. UDA methods try to align the source and target representations to improve the generalization on the target domain. The Source-Free Adaptation Domain (SFDA) setting aims to alleviate these concerns by adapting a source-trained model for the target domain without requiring access to the source data.
  arXiv  Detail & Related papers  (2022-03-29T17:50:43Z)
• 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)
• Revisiting Deep Subspace Alignment for Unsupervised Domain Adaptation [42.16718847243166]
  Unsupervised domain adaptation (UDA) aims to transfer and adapt knowledge from a labeled source domain to an unlabeled target domain. Traditionally, subspace-based methods form an important class of solutions to this problem. This paper revisits the use of subspace alignment for UDA and proposes a novel adaptation algorithm that consistently leads to improved generalization.
  arXiv  Detail & Related papers  (2022-01-05T20:16:38Z)
• Adversarially Trained Object Detector for Unsupervised Domain Adaptation [4.9631159466100305]
  We demonstrate that adversarial training in the source domain can be employed as a new approach for unsupervised domain adaptation. We propose a method that combines adversarial training and feature alignment to ensure the improved alignment of robust features with the target domain.
  arXiv  Detail & Related papers  (2021-09-13T07:21:28Z)
• Instance Level Affinity-Based Transfer for Unsupervised Domain Adaptation [74.71931918541748]
  We propose an instance affinity based criterion for source to target transfer during adaptation, called ILA-DA. We first propose a reliable and efficient method to extract similar and dissimilar samples across source and target, and utilize a multi-sample contrastive loss to drive the domain alignment process. We verify the effectiveness of ILA-DA by observing consistent improvements in accuracy over popular domain adaptation approaches on a variety of benchmark datasets.
  arXiv  Detail & Related papers  (2021-04-03T01:33:14Z)

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.