Results 11 to 20 of about 1,469,780 (320)
YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors [PDF]
Computer Vision and Pattern Recognition, 2022 Real-time object detection is one of the most important research topics in computer vision. As new approaches regarding architecture optimization and training optimization are continually being developed, we have found two research topics that have ...
Chien-Yao Wang +2 more
semanticscholar +1 more source
RTMDet: An Empirical Study of Designing Real-Time Object Detectors [PDF]
arXiv.org, 2022 In this paper, we aim to design an efficient real-time object detector that exceeds the YOLO series and is easily extensible for many object recognition tasks such as instance segmentation and rotated object detection.
Chengqi Lyu +7 more
semanticscholar +1 more source
Focal and Global Knowledge Distillation for Detectors [PDF]
Computer Vision and Pattern Recognition, 2021 Knowledge distillation has been applied to image classification successfully. However, object detection is much more sophisticated and most knowledge distillation methods have failed on it.
Zhendong Yang +6 more
semanticscholar +1 more source
DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution [PDF]
Computer Vision and Pattern Recognition, 2020 Many modern object detectors demonstrate outstanding performances by using the mechanism of looking and thinking twice. In this paper, we explore this mechanism in the backbone design for object detection. At the macro level, we propose Recursive Feature
Siyuan Qiao, Liang-Chieh Chen, A. Yuille
semanticscholar +1 more source
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters [PDF]
International Conference on Information and Knowledge Management, 2020 Graph Neural Networks (GNNs) have been widely applied to fraud detection problems in recent years, revealing the suspiciousness of nodes by aggregating their neighborhood information via different relations.
Yingtong Dou +5 more
semanticscholar +1 more source
Adversarial Texture for Fooling Person Detectors in the Physical World [PDF]
Computer Vision and Pattern Recognition, 2022 Nowadays, cameras equipped with AI systems can capture and analyze images to detect people automatically. However, the AI system can make mistakes when receiving deliberately designed patterns in the real world, i.e., physical adversarial examples. Prior
Zhan Hu +5 more
semanticscholar +1 more source
npj Digit. Medicine, 2023 Large language models such as ChatGPT can produce increasingly realistic text, with unknown information on the accuracy and integrity of using these models in scientific writing.
C. Gao +6 more
semanticscholar +1 more source
Distilling Object Detectors via Decoupled Features [PDF]
Computer Vision and Pattern Recognition, 2021 Knowledge distillation is a widely used paradigm for inheriting information from a complicated teacher network to a compact student network and maintaining the strong performance.
Jianyuan Guo +6 more
semanticscholar +1 more source
Training Region-Based Object Detectors with Online Hard Example Mining [PDF]
Computer Vision and Pattern Recognition, 2016 The field of object detection has made significant advances riding on the wave of region-based ConvNets, but their training procedure still includes many heuristics and hyperparameters that are costly to tune.
Abhinav Shrivastava +2 more
semanticscholar +1 more source
Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors [PDF]
Computer Vision and Pattern Recognition, 2016 The goal of this paper is to serve as a guide for selecting a detection architecture that achieves the right speed/memory/accuracy balance for a given application and platform.
Jonathan Huang +10 more
semanticscholar +1 more source