Domain Adaptation For Object Detection

The object detection task assumes that training and test data are drawn from the same distribution.

Domain adaptation for object detection. Morariu behjat siddiquie2 rogerio s. Domain adaptation for object recognition. Progressive domain adaptation for object detection. We find that enforcing low distance in the.

We propose a domain adaptation approach for object detection. Recent deep learning methods for object detection rely on a large amount of bounding box annotations. Collecting these annotations is laborious and costly yet supervised models do not generalize well when testing on images from a different distribution. Implementation of our paper progressive domain adaptation for object detection based on pytorch faster rcnn and pytorch cyclegan.

Domain adaptation provides a solution by adapting existing labels to the target testing data. This paper considers distribution matching in various feature level for unsupervised domain adaptation for object detection with a single stage detector. This paper presents a novel uda model which integrates both image and feature level based adaptations to solve the cross domain object detection problem. Many unsupervised domain adaptation uda methods were introduced to address this problem but most of them only focused on the simple classification task.

We introduce a two step method. Progressive domain adaptation for object detection han kai hsu chun han yao yi hsuan tsai wei chih hung hung yu tseng maneesh singh and ming hsuan yang ieee winter conference on applications of. However a large gap between domains could make. However in a real environment there is a domain gap between training and test data which leads to degrading performance significantly.

Domain adaptive faster r cnn for object detection in the wild yuhua chen1 wen li1 christos sakaridis1 dengxin dai1 luc van gool1 2 1computer vision lab eth zurich 2visics esat psi ku leuven yuhua chen liwen csakarid dai vangool vision ee ethz ch abstract object detection typically assumes that training and test. Progressive domain adaptation for object detection han kai hsu1 wei chih hung1 hung yu tseng1 chun han yao2 yi hsuan tsai3 maneesh singh4 ming hsuan yang1 5 1university of california merced 2university of california san diego 3nec laboratories america 4verisk analytics 5google abstract recent deep learning methods for object detection rely on a large amount of bounding box annotations. The first step makes the detector robust to low level differences and the second step adapts the classifiers to changes in the high level features. Domain adaptive object detection fatemeh mirrashed 1 vlad i.