Integrated Multiscale Domain Adaptive YOLO
Mazin Hnewa, Hayder Radha

TL;DR
This paper introduces MS-DAYOLO, a multiscale domain adaptive object detection framework based on YOLOv4, which improves detection accuracy across domains while maintaining real-time speed suitable for autonomous driving.
Contribution
The paper presents three novel deep learning architectures for domain adaptation integrated into YOLOv4, enhancing cross-domain object detection performance.
Findings
Significant improvement in detection accuracy on target domain datasets.
Order of magnitude faster than Faster R-CNN with comparable accuracy.
Effective multiscale domain adaptation for autonomous driving applications.
Abstract
The area of domain adaptation has been instrumental in addressing the domain shift problem encountered by many applications. This problem arises due to the difference between the distributions of source data used for training in comparison with target data used during realistic testing scenarios. In this paper, we introduce a novel MultiScale Domain Adaptive YOLO (MS-DAYOLO) framework that employs multiple domain adaptation paths and corresponding domain classifiers at different scales of the recently introduced YOLOv4 object detector. Building on our baseline multiscale DAYOLO framework, we introduce three novel deep learning architectures for a Domain Adaptation Network (DAN) that generates domain-invariant features. In particular, we propose a Progressive Feature Reduction (PFR), a Unified Classifier (UC), and an Integrated architecture. We train and test our proposed DAN…
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Taxonomy
TopicsDomain Adaptation and Few-Shot Learning · Advanced Neural Network Applications · COVID-19 diagnosis using AI
Methods(TravEL!!Guide)How Do I File a Claim with Expedia? · *Communicated@Fast*How Do I Communicate to Expedia? · SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings · Feature Pyramid Network · You Only Look Once · Batch Normalization · Global Average Pooling · Residual Connection · BNB Customer Service Number +1-833-534-1729 · k-Means Clustering
