Instance Relation Graph Guided Source-Free Domain Adaptive Object
Detection
- URL: http://arxiv.org/abs/2203.15793v4
- Date: Tue, 21 Mar 2023 16:06:20 GMT
- Title: Instance Relation Graph Guided Source-Free Domain Adaptive Object
Detection
- Authors: Vibashan VS, Poojan Oza and Vishal M. Patel
- Abstract summary: 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.
- Score: 79.89082006155135
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Unsupervised Domain Adaptation (UDA) is an effective approach to tackle the
issue of domain shift. Specifically, UDA methods try to align the source and
target representations to improve the generalization on the target domain.
Further, UDA methods work under the assumption that the source data is
accessible during the adaptation process. However, in real-world scenarios, the
labelled source data is often restricted due to privacy regulations, data
transmission constraints, or proprietary data concerns. The Source-Free Domain
Adaptation (SFDA) setting aims to alleviate these concerns by adapting a
source-trained model for the target domain without requiring access to the
source data. In this paper, we explore the SFDA setting for the task of
adaptive object detection. To this end, we propose a novel training strategy
for adapting a source-trained object detector to the target domain without
source data. More precisely, we design a novel contrastive loss to enhance the
target representations by exploiting the objects relations for a given target
domain input. These object instance relations are modelled using an Instance
Relation Graph (IRG) network, which are then used to guide the contrastive
representation learning. In addition, we utilize a student-teacher based
knowledge distillation strategy to avoid overfitting to the noisy pseudo-labels
generated by the source-trained model. Extensive experiments on multiple object
detection benchmark datasets show that the proposed approach is able to
efficiently adapt source-trained object detectors to the target domain,
outperforming previous state-of-the-art domain adaptive detection methods. Code
and models are provided in
\href{https://viudomain.github.io/irg-sfda-web/}{https://viudomain.github.io/irg-sfda-web/}.
Related papers
- Transcending Domains through Text-to-Image Diffusion: A Source-Free
Approach to Domain Adaptation [6.649910168731417]
Domain Adaptation (DA) is a method for enhancing a model's performance on a target domain with inadequate annotated data.
We propose a novel framework for SFDA that generates source data using a text-to-image diffusion model trained on the target domain samples.
arXiv Detail & Related papers (2023-10-02T23:38:17Z) - SF-FSDA: Source-Free Few-Shot Domain Adaptive Object Detection with
Efficient Labeled Data Factory [94.11898696478683]
Domain adaptive object detection aims to leverage the knowledge learned from a labeled source domain to improve the performance on an unlabeled target domain.
We propose and investigate a more practical and challenging domain adaptive object detection problem under both source-free and few-shot conditions, named as SF-FSDA.
arXiv Detail & Related papers (2023-06-07T12:34:55Z) - Towards Source-free Domain Adaptive Semantic Segmentation via Importance-aware and Prototype-contrast Learning [26.544837987747766]
We propose an end-to-end source-free domain adaptation semantic segmentation method via Importance-Aware and Prototype-Contrast learning.
The proposed IAPC framework effectively extracts domain-invariant knowledge from the well-trained source model and learns domain-specific knowledge from the unlabeled target domain.
arXiv Detail & Related papers (2023-06-02T15:09:19Z) - Spatio-Temporal Pixel-Level Contrastive Learning-based Source-Free
Domain Adaptation for Video Semantic Segmentation [117.39092621796753]
Source Domain Adaptation (SFDA) setup aims to adapt a source-trained model to the target domain without accessing source data.
A novel method that takes full advantage of correlations oftemporal-information to tackle the absence of source data is proposed.
Experiments show that PixelL achieves un-of-the-art performance on benchmarks compared to current UDA and SFDA approaches.
arXiv Detail & Related papers (2023-03-25T05:06:23Z) - 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) - Source-Free Domain Adaptation for Semantic Segmentation [11.722728148523366]
Unsupervised Domain Adaptation (UDA) can tackle the challenge that convolutional neural network-based approaches for semantic segmentation heavily rely on the pixel-level annotated data.
We propose a source-free domain adaptation framework for semantic segmentation, namely SFDA, in which only a well-trained source model and an unlabeled target domain dataset are available for adaptation.
arXiv Detail & Related papers (2021-03-30T14:14:29Z) - Source Data-absent Unsupervised Domain Adaptation through Hypothesis
Transfer and Labeling Transfer [137.36099660616975]
Unsupervised adaptation adaptation (UDA) aims to transfer knowledge from a related but different well-labeled source domain to a new unlabeled target domain.
Most existing UDA methods require access to the source data, and thus are not applicable when the data are confidential and not shareable due to privacy concerns.
This paper aims to tackle a realistic setting with only a classification model available trained over, instead of accessing to the source data.
arXiv Detail & Related papers (2020-12-14T07:28:50Z) - Do We Really Need to Access the Source Data? Source Hypothesis Transfer
for Unsupervised Domain Adaptation [102.67010690592011]
Unsupervised adaptationUDA (UDA) aims to leverage the knowledge learned from a labeled source dataset to solve similar tasks in a new unlabeled domain.
Prior UDA methods typically require to access the source data when learning to adapt the model.
This work tackles a practical setting where only a trained source model is available and how we can effectively utilize such a model without source data to solve UDA problems.
arXiv Detail & Related papers (2020-02-20T03:13:58Z)
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.