Unsupervised Domain Adaption of Object Detectors: A Survey
- URL: http://arxiv.org/abs/2105.13502v1
- Date: Thu, 27 May 2021 23:34:06 GMT
- Title: Unsupervised Domain Adaption of Object Detectors: A Survey
- Authors: Poojan Oza, Vishwanath A. Sindagi, Vibashan VS, Vishal M. Patel
- Abstract summary: Recent advances in deep learning have led to the development of accurate and efficient models for various computer vision applications.
Learning highly accurate models relies on the availability of datasets with a large number of annotated images.
Due to this, model performance drops drastically when evaluated on label-scarce datasets having visually distinct images.
- Score: 87.08473838767235
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent advances in deep learning have led to the development of accurate and
efficient models for various computer vision applications such as object
classification, semantic segmentation, and object detection. However, learning
highly accurate models relies on the availability of datasets with a large
number of annotated images. Due to this, model performance drops drastically
when evaluated on label-scarce datasets having visually distinct images. This
issue is commonly referred to as covariate shift or dataset bias. Domain
adaptation attempts to address this problem by leveraging domain shift
characteristics from labeled data in a related domain when learning a
classifier for label-scarce target dataset. There are a plethora of works to
adapt object classification and semantic segmentation models to label-scarce
target dataset through unsupervised domain adaptation. Considering that object
detection is a fundamental task in computer vision, many recent works have
recently focused on addressing the domain adaptation issue for object detection
as well. In this paper, we provide a brief introduction to the domain
adaptation problem for object detection and present an overview of various
methods proposed to date for addressing this problem. Furthermore, we highlight
strategies proposed for this problem and the associated shortcomings.
Subsequently, we identify multiple aspects of the unsupervised domain adaptive
detection problem that are most promising for future research in the area. We
believe that this survey shall be valuable to the pattern recognition experts
working in the fields of computer vision, biometrics, medical imaging, and
autonomous navigation by introducing them to the problem, getting them familiar
with the current status of the progress, and providing them with promising
direction for future research.
Related papers
- Deep Learning-Based Object Pose Estimation: A Comprehensive Survey [73.74933379151419]
We discuss the recent advances in deep learning-based object pose estimation.
Our survey also covers multiple input data modalities, degrees-of-freedom of output poses, object properties, and downstream tasks.
arXiv Detail & Related papers (2024-05-13T14:44:22Z) - Zero-shot Degree of Ill-posedness Estimation for Active Small Object Change Detection [8.977792536037956]
In everyday indoor navigation, robots often needto detect non-distinctive small-change objects.
Existing techniques rely on high-quality class-specific object priors to regularize a change detector model.
In this study, we explore the concept of degree-of-ill-posedness (DoI) to improve both passive and activevision.
arXiv Detail & Related papers (2024-05-10T01:56:39Z) - Labeling Where Adapting Fails: Cross-Domain Semantic Segmentation with
Point Supervision via Active Selection [81.703478548177]
Training models dedicated to semantic segmentation require a large amount of pixel-wise annotated data.
Unsupervised domain adaptation approaches aim at aligning the feature distributions between the labeled source and the unlabeled target data.
Previous works attempted to include human interactions in this process under the form of sparse single-pixel annotations in the target data.
We propose a new domain adaptation framework for semantic segmentation with annotated points via active selection.
arXiv Detail & Related papers (2022-06-01T01:52:28Z) - Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D
Object Detection [85.11649974840758]
3D object detection networks tend to be biased towards the data they are trained on.
We propose a single-frame approach for source-free, unsupervised domain adaptation of lidar-based 3D object detectors.
arXiv Detail & Related papers (2021-11-30T18:42:42Z) - A Survey of Self-Supervised and Few-Shot Object Detection [19.647681501581225]
Self-supervised methods aim at learning representations from unlabeled data which transfer well to downstream tasks such as object detection.
Few-shot object detection is about training a model on novel (unseen) object classes with little data.
In this survey, we review and characterize the most recent approaches on few-shot and self-supervised object detection.
arXiv Detail & Related papers (2021-10-27T18:55:47Z) - A Review of Single-Source Deep Unsupervised Visual Domain Adaptation [81.07994783143533]
Large-scale labeled training datasets have enabled deep neural networks to excel across a wide range of benchmark vision tasks.
In many applications, it is prohibitively expensive and time-consuming to obtain large quantities of labeled data.
To cope with limited labeled training data, many have attempted to directly apply models trained on a large-scale labeled source domain to another sparsely labeled or unlabeled target domain.
arXiv Detail & Related papers (2020-09-01T00:06:50Z) - Co-training for On-board Deep Object Detection [0.0]
Best performing deep vision-based object detectors are trained in a supervised manner by relying on human-labeled bounding boxes.
Co-training is a semi-supervised learning method for self-labeling objects in unlabeled images.
We show how co-training is a paradigm worth to pursue for alleviating object labeling, working both alone and together with task-agnostic domain adaptation.
arXiv Detail & Related papers (2020-08-12T19:08:59Z) - One-Shot Unsupervised Cross-Domain Detection [33.04327634746745]
This paper presents an object detection algorithm able to perform unsupervised adaption across domains by using only one target sample, seen at test time.
We achieve this by introducing a multi-task architecture that one-shot adapts to any incoming sample by iteratively solving a self-supervised task on it.
arXiv Detail & Related papers (2020-05-23T22:12:20Z) - Adaptive Object Detection with Dual Multi-Label Prediction [78.69064917947624]
We propose a novel end-to-end unsupervised deep domain adaptation model for adaptive object detection.
The model exploits multi-label prediction to reveal the object category information in each image.
We introduce a prediction consistency regularization mechanism to assist object detection.
arXiv Detail & Related papers (2020-03-29T04:23:22Z)
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.