Results 11 to 20 of about 7,363,707 (395)
DETRs Beat YOLOs on Real-time Object Detection [PDF]
Computer Vision and Pattern Recognition, 2023 The YOLO series has become the most popular frame-work for real-time object detection due to its reasonable trade-off between speed and accuracy. However, we observe that the speed and accuracy of YOLOs are negatively affected by the NMS.
Wenyu Lv +8 more
semanticscholar +1 more source
IET Biometrics, 2022 Breast cancer accounts for the largest number of patients among all cancers in the world. Intervention treatment for early breast cancer can dramatically extend a woman's 5‐year survival rate.
Lilei Sun +6 more
doaj +1 more source
CGENet: A Deep Graph Model for COVID-19 Detection Based on Chest CT
Biology, 2021 Accurate and timely diagnosis of COVID-19 is indispensable to control its spread. This study proposes a novel explainable COVID-19 diagnosis system called CGENet based on graph embedding and an extreme learning machine for chest CT images. We put forward
Si-Yuan Lu +3 more
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Two‐view attention‐guided convolutional neural network for mammographic image classification
CAAI Transactions on Intelligence Technology, 2023 Deep learning has been widely used in the field of mammographic image classification owing to its superiority in automatic feature extraction. However, general deep learning models cannot achieve very satisfactory classification results on mammographic ...
Lilei Sun +6 more
doaj +1 more source
Object Detection: An Overview [PDF]
International Journal of Trend in Scientific Research and Development, 2019 The goal of the project is to run an object detection algorithm on every frame of a video, thus allowing the algorithm to detect all the objects in it, including but not limited to people, vehicles, animals etc. Object recognition and detection play a crucial role in computer vision and automated driving systems. We aim to design a system that does not
P. Rajeshwari +2 more
openaire +2 more sources
FCOS: Fully Convolutional One-Stage Object Detection [PDF]
IEEE International Conference on Computer Vision, 2019 We propose a fully convolutional one-stage object detector (FCOS) to solve object detection in a per-pixel prediction fashion, analogue to semantic segmentation.
Zhi Tian +3 more
semanticscholar +1 more source
Cut and Learn for Unsupervised Object Detection and Instance Segmentation [PDF]
Computer Vision and Pattern Recognition, 2023 We propose Cut-and-LEaRn (CutLER), a simple approach for training unsupervised object detection and seg-mentation models. We leverage the property of self-supervised models to ‘discover’ objects without supervision and amplify it to train a state-of-the ...
Xudong Wang +3 more
semanticscholar +1 more source
Video Sparse Transformer With Attention-Guided Memory for Video Object Detection
IEEE Access, 2022 Detecting objects in a video, known as Video Object Detection (VOD), is challenging since appearance changes of objects over time may bring detection errors.
Masato Fujitake, Akihiro Sugimoto
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EfficientDet: Scalable and Efficient Object Detection [PDF]
Computer Vision and Pattern Recognition, 2019 Model efficiency has become increasingly important in computer vision. In this paper, we systematically study neural network architecture design choices for object detection and propose several key optimizations to improve efficiency. First, we propose a
Mingxing Tan, Ruoming Pang, Quoc V. Le
semanticscholar +1 more source
Cascade R-CNN: Delving Into High Quality Object Detection [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017 In object detection, an intersection over union (IoU) threshold is required to define positives and negatives. An object detector, trained with low IoU threshold, e.g. 0.5, usually produces noisy detections.
Zhaowei Cai, N. Vasconcelos
semanticscholar +1 more source