Densely Semantic Enhancement for Domain Adaptive Region-free Detectors

by   Bo Zhang, et al.

Unsupervised domain adaptive object detection aims to adapt a well-trained detector from its original source domain with rich labeled data to a new target domain with unlabeled data. Previous works focus on improving the domain adaptability of region-based detectors, e.g., Faster-RCNN, through matching cross-domain instance-level features that are explicitly extracted from a region proposal network (RPN). However, this is unsuitable for region-free detectors such as single shot detector (SSD), which perform a dense prediction from all possible locations in an image and do not have the RPN to encode such instance-level features. As a result, they fail to align important image regions and crucial instance-level features between the domains of region-free detectors. In this work, we propose an adversarial module to strengthen the cross-domain matching of instance-level features for region-free detectors. Firstly, to emphasize the important regions of image, the DSEM learns to predict a transferable foreground enhancement mask that can be utilized to suppress the background disturbance in an image. Secondly, considering that region-free detectors recognize objects of different scales using multi-scale feature maps, the DSEM encodes both multi-level semantic representations and multi-instance spatial-contextual relationships across different domains. Finally, the DSEM is pluggable into different region-free detectors, ultimately achieving the densely semantic feature matching via adversarial learning. Extensive experiments have been conducted on PASCAL VOC, Clipart, Comic, Watercolor, and FoggyCityscape benchmarks, and their results well demonstrate that the proposed approach not only improves the domain adaptability of region-free detectors but also outperforms existing domain adaptive region-based detectors under various domain shift settings.



There are no comments yet.


page 1

page 2

page 3

page 9

page 11

page 12


Joint Distribution Alignment via Adversarial Learning for Domain Adaptive Object Detection

Unsupervised domain adaptive object detection aims to adapt a well-train...

RPCL: A Framework for Improving Cross-Domain Detection with Auxiliary Tasks

Cross-Domain Detection (XDD) aims to train an object detector using labe...

Domain Adaptive Object Detection via Feature Separation and Alignment

Recently, adversarial-based domain adaptive object detection (DAOD) meth...

Adapting Object Detectors with Conditional Domain Normalization

Real-world object detectors are often challenged by the domain gaps betw...

Domain Contrast for Domain Adaptive Object Detection

We present Domain Contrast (DC), a simple yet effective approach inspire...

Domain Adaptive YOLO for One-Stage Cross-Domain Detection

Domain shift is a major challenge for object detectors to generalize wel...

I^3Net: Implicit Instance-Invariant Network for Adapting One-Stage Object Detectors

Recent works on two-stage cross-domain detection have widely explored th...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.