Domain Adaptation Object Detection Github

Starting from a fully supervised object detector which is pre trained on the source domain we propose a two step progressive domain adaptation technique by fine tuning the detector on two types of artificially and automatically generated samples.

Domain adaptation object detection github. Recently with the advent of deep learning there are a number of techniques that propose methods for deep learning based domain adaptation. This page is for the paper appeared in cvpr2018. In addition all the target domain classes to be detected or a subset of them are in the source domain. Cross domain weakly supervised object detection through progressive domain adaptation.

Towards real time object detection with region proposal networks. Kim et al. This is undesirable for autonomous systems like robots or self driving cars where determining the domain is part of the problem to solve. Hsu et al.

Ren et al. However the methods proposed have been used for adapting object classification techniques. You can also find project page for the paper. Deep salient object detection with dense connections and distraction diagnosis huaxin xiao jiashi feng yunchao wei maojun zhang.

Progressive domain adaptation for object detection. Towards accurate domain adaptive object detection via gradient detach based stacked complementary losses 6 nov 2019. Domain adaptation da refers to a set of transfer learning techniques developed to update the data distribution in sim to match the real one through a mapping or regularization enforced by the task model. Unsupervised domain adaptation for object detection via cross domain semi supervised learning 17 nov 2019 curriculum self paced learning for cross domain object detection 15 nov 2019 scl.

Many da models especially for image classification or end to end image based rl task are built on adversarial loss or gan. The detectors of the adaptive multi domain detector require prior knowledge of the domain of interest. Domain adaptive faster r cnn for object detection in the wild 1. Here is the example of our results in watercolor images.

Unsupervised domain adaptation uda is a promising approach to adapt models for new domains environments without any expensive label cost. Contrastive adaptation network for single and multi source domain adaptation guoliang kang lu jiang yunchao wei yi yang alexander g. Domain adaptation has been understood and adopted in vision. Self training and adversarial background regularization for unsupervised domain adaptive one stage object detection.