Generative and self-supervised domain adaptation for one-stage object detection

Fujii, Kazuma; Kawamoto, Kazuhiko

doi:10.1016/j.array.2021.100071

Cited by 7 publications

(4 citation statements)

References 3 publications

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“…x Mish x e =  + robustness of network training and enables the learning of more deep contexts; instead of using the operation of YOLO to predict the object after the full connection layer [61,68,69], CNN is added to the backbone network to predict directly. Combined with the anchor mechanism in Faster R-CNN, the candidate regions are obtained by using different prior boxes, and the recall rate is improved.…”

Section: Yoloxmentioning

confidence: 99%

Review on One-Stage Object Detection Based on Deep Learning

Zhang¹,

Cloutier²

2022

EAI Endorsed Transactions on e-Learning

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As a popular research direction in computer vision, deep learning technology has promoted breakthroughs in the field of object detection. In recent years, the combination of object detection and the Internet of Things (IoT) has been widely used in the fields of face recognition, pedestrian detection, unmanned driving, and customs detection. With the development of object detection, two different detection algorithms, one-stage, and two-stage have gradually formed. This paper mainly introduces the one-stage object detection algorithm. Firstly, the development process of the convolutional neural network is briefly reviewed, Then, the current mainstream one-stage object detection model is summarized. Based on YOLOv1, it is continuously optimized, and the improvements and shortcomings are summarized in detail. Finally, a summary is made based on the difficulties and challenges of one-stage object detection algorithms.

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Section: Yoloxmentioning

confidence: 99%

Review on One-Stage Object Detection Based on Deep Learning

Zhang¹,

Cloutier²

2022

EAI Endorsed Transactions on e-Learning

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“…We consider the classic DG evaluation method to evaluate detectors' generalization ability, where one dataset is selected for test and the others for training for each run. Note that for DG evaluation, knowledge of test distribution is completely inaccessible in the training phase, so that current detection methods designed for domain adaptation (DA) [17,24,34,55] are not applicable. Moreover, most current DG methods are designed for image classification and the adaptation of them in the object detection task is nontrivial [41,60,75].…”

Section: Classic Dg Evaluation In General Object Detectionmentioning

confidence: 99%

Towards Domain Generalization in Object Detection

Zhang¹,

Xu²,

Xu³

et al. 2022

Preprint

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Despite the striking performance achieved by modern detectors when training and test data are sampled from the same or similar distribution, the generalization ability of detectors under unknown distribution shifts remains hardly studied. Recently several works discussed the detectors' adaptation ability to a specific target domain, which are not readily applicable in real-world applications since detectors may encounter various environments or situations while pre-collecting all of them before training is inconceivable. In this paper, we study the critical problem, domain generalization in object detection (DGOD), where detectors are trained with source domains and evaluated on unknown target domains. To thoroughly evaluate detectors under unknown distribution shifts, we formulate the DGOD problem and propose a comprehensive evaluation benchmark to fill the vacancy. Moreover, we propose a novel method named Region Aware Proposal reweighTing (RAPT) to eliminate dependence within RoI features. Extensive experiments demonstrate that current DG methods fail to address the DGOD problem and our method outperforms other state-of-the-art counterparts.

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“…As a preliminary phase, proposals and regional classification are made [55]. Fast/Faster Recurrent Convolutional Neural Network (RCNN two-stage detector) is preferable for immediate information access since the bounding box and object class estimation processes are carried out in tandem [56].…”

Section: State-of-the-art Object Detection Algorithmsmentioning

confidence: 99%

A Semi-Supervised Learning Approach for Automatic Detection and Fashion Product Category Prediction with Small Training Dataset Using FC-YOLOv4

2022

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Over the past few decades, research on object detection has developed rapidly, one of which can be seen in the fashion industry. Fast and accurate detection of an E-commerce fashion product is crucial to choosing the appropriate category. Nowadays, both new and second-hand clothing is provided by E-commerce sites for purchase. Therefore, when categorizing fashion clothing, it is essential to categorize it precisely, regardless of the cluttered background. We present recently acquired tiny product images with various resolutions, sizes, and positions datasets from the Shopee E-commerce (Thailand) website. This paper also proposes the Fashion Category—You Only Look Once version 4 model called FC-YOLOv4 for detecting multiclass fashion products. We used the semi-supervised learning approach to reduce image labeling time, and the number of resulting images is then increased through image augmentation. This approach results in reasonable Average Precision (AP), Mean Average Precision (mAP), True or False Positive (TP/FP), Recall, Intersection over Union (IoU), and reliable object detection. According to experimental findings, our model increases the mAP by 0.07 percent and 40.2 percent increment compared to the original YOLOv4 and YOLOv3. Experimental findings from our FC-YOLOv4 model demonstrate that it can effectively provide accurate fashion category detection for properly captured and clutter images compared to the YOLOv4 and YOLOv3 models.

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Generative and self-supervised domain adaptation for one-stage object detection

Cited by 7 publications

References 3 publications

Review on One-Stage Object Detection Based on Deep Learning

Review on One-Stage Object Detection Based on Deep Learning

Towards Domain Generalization in Object Detection

A Semi-Supervised Learning Approach for Automatic Detection and Fashion Product Category Prediction with Small Training Dataset Using FC-YOLOv4

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