4.7 Article

Decompose to Adapt: Cross-Domain Object Detection Via Feature Disentanglement

Related references

Note: Only part of the references are listed.
Article Computer Science, Information Systems

Uncertainty-Aware Unsupervised Domain Adaptation in Object Detection

Dayan Guan et al.

Summary: This paper proposes an uncertainty-aware domain adaptation network (UaDAN) that introduces conditional adversarial learning to align well-aligned and poorly-aligned samples separately. By utilizing an uncertainty metric, UaDAN achieves superior performance through curriculum learning strategy.

IEEE TRANSACTIONS ON MULTIMEDIA (2022)

Article Computer Science, Information Systems

Informative Feature Disentanglement for Unsupervised Domain Adaptation

Wanxia Deng et al.

Summary: Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain with a related but different distribution. Existing methods have made progress in aligning the two domains in latent feature space, but they mainly focus on adapting the entire image and ignore the negative effect of uninformative domain-specific variations on learned features. To address this issue, this paper proposes a novel component called Informative Feature Disentanglement (IFD) equipped with adversarial networks or metric discrepancy models. The proposed IFDAN and IFDMN models refine informative features before adaptation, effectively disentangling them from uninformative domain-specific variations. Extensive experimental results on three gold-standard domain adaptation datasets demonstrate the effectiveness of the proposed IFDAN and IFDMN models for UDA.

IEEE TRANSACTIONS ON MULTIMEDIA (2022)

Article Computer Science, Artificial Intelligence

Instance-Invariant Domain Adaptive Object Detection Via Progressive Disentanglement

Aming Wu et al.

Summary: The paper addresses the issue of poor generalization ability in object detection methods when dealing with domain shift. A method is proposed to extract instance-invariant features and enhance the disentangled ability of the model, leading to improved performance compared to baseline methods. Visualization analysis confirms the effectiveness of the proposed model.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2022)

Article Computer Science, Interdisciplinary Applications

PDAM: A Panoptic-Level Feature Alignment Framework for Unsupervised Domain Adaptive Instance Segmentation in Microscopy Images

Dongnan Liu et al.

Summary: In this work, we propose a Panoptic Domain Adaptive Mask R-CNN (PDAM) method for unsupervised instance segmentation in microscopy images. By integrating semantic- and instance-level feature adaptation, our method effectively aligns cross-domain features at the panoptic level and solves domain bias issues. Experimental results demonstrate that the PDAM method outperforms state-of-the-art UDA methods by a large margin in various scenarios.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2021)

Article Computer Science, Artificial Intelligence

CDTD: A Large-Scale Cross-Domain Benchmark for Instance-Level Image-to-Image Translation and Domain Adaptive Object Detection

Zhiqiang Shen et al.

Summary: The study introduces a new large-scale cross-domain benchmark dataset CDTD for instance-level image-to-image translation and object detection tasks. Comprehensive benchmark results are provided, along with a novel instance-level image-to-image translation method and a gradient detach method.

INTERNATIONAL JOURNAL OF COMPUTER VISION (2021)

Proceedings Paper Computer Science, Artificial Intelligence

Domain-Invariant Disentangled Network for Generalizable Object Detection

Chuang Lin et al.

Summary: This research focuses on domain generalizable object detection and introduces a novel model called DIDN, which utilizes a disentangled network to learn domain-invariant representations suitable for generalized object detection. By integrating a disentangled network into Faster R-CNN, the model is able to address domain gaps on both image and instance levels effectively. Extensive experiments show that the model achieves state-of-the-art performances on domain generalization for object detection.

2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021) (2021)

Proceedings Paper Computer Science, Artificial Intelligence

RDA: Robust Domain Adaptation via Fourier Adversarial Attacking

Jiaxing Huang et al.

Summary: This paper introduces a robust domain adaptation technique RDA, which mitigates overfitting in unsupervised domain adaptation through adversarial attacking. By using Fourier adversarial attacking method to generate adversarial samples, training can become more robust and achieve superior performance across multiple computer vision tasks.

2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021) (2021)

Proceedings Paper Computer Science, Artificial Intelligence

Domain-Specific Suppression for Adaptive Object Detection

Yu Wang et al.

Summary: This study presents a new perspective on how CNN models gain transferability by distinguishing and suppressing domain-specific directions to optimize domain adaptation in object detection. Experimental results demonstrate that the domain-specific suppression method significantly improves object detection performance, with an increase in mAP by 10.2 to 12.2%.

2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021 (2021)

Article Computer Science, Information Systems

Adversarial Network With Multiple Classifiers for Open Set Domain Adaptation

Tasfia Shermin et al.

Summary: Domain adaptation aims to transfer knowledge from a domain with adequate labeled samples to a domain with scarce labeled samples. This paper focuses on an open set domain adaptation setting where the target domain has both private and shared label space, while the source domain only has the shared label space. Traditional distribution-matching domain adaptation methods are inadequate for addressing this specific scenario.

IEEE TRANSACTIONS ON MULTIMEDIA (2021)

Article Computer Science, Information Systems

SPA-GAN: Spatial Attention GAN for Image-to-Image Translation

Hajar Emami et al.

Summary: This paper introduces a novel SPA-GAN model with attention mechanism for improved image-to-image translation tasks, as well as an additional feature map loss during training to preserve domain specific features. Compared to existing attention-guided GAN models, SPA-GAN demonstrates superior performance without the need for additional attention networks or supervision.

IEEE TRANSACTIONS ON MULTIMEDIA (2021)

Proceedings Paper Computer Science, Artificial Intelligence

Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-weighting

Dongnan Liu et al.

2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2020)

Article Computer Science, Artificial Intelligence

Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach

Behnam Gholami et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2020)

Proceedings Paper Computer Science, Artificial Intelligence

Adapting Object Detectors via Selective Cross-Domain Alignment

Xinge Zhu et al.

2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) (2019)

Proceedings Paper Computer Science, Artificial Intelligence

Attending to Discriminative Certainty for Domain Adaptation

Vinod Kumar Kurmi et al.

2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) (2019)

Article Computer Science, Artificial Intelligence

Semantic Foggy Scene Understanding with Synthetic Data

Christos Sakaridis et al.

INTERNATIONAL JOURNAL OF COMPUTER VISION (2018)

Article Computer Science, Artificial Intelligence

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Shaoqing Ren et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2017)

Proceedings Paper Computer Science, Artificial Intelligence

Fast R-CNN

Ross Girshick

2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) (2015)

Proceedings Paper Computer Science, Artificial Intelligence

Unsupervised Visual Domain Adaptation Using Subspace Alignment

Basura Fernando et al.

2013 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) (2013)

Article Computer Science, Artificial Intelligence

A Survey on Transfer Learning

Sinno Jialin Pan et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2010)

Article Computer Science, Artificial Intelligence

The Earth Mover's Distance as a metric for image retrieval

Y Rubner et al.

INTERNATIONAL JOURNAL OF COMPUTER VISION (2000)