AI Technology & Industry Review
In the new paper DETRs Beat YOLOs on Real-Time Object Detection, a Baidu Inc. research team presents Real-Time Detection Transformer (RT-DETR), a real-time end-to-end object detector that leverages a hybrid encoder and novel IoU-aware query selection to address inference speed delay issues. RT-DETR outperforms YOLO object detectors in both accuracy and speed.
In the new paper YOLOv7: Trainable Bag-Of-Freebies Sets New State-Of-The-Art for Real-Time Object Detectors, an Academia Sinica research team releases YOLOv7. This latest YOLO version introduces novel “extend” and “compound scaling” methods that effectively utilize parameters and computation; and surpasses all known real-time object detectors in speed and accuracy.
A Meta AI research team explores the plain, non-hierarchical vision transformer (ViT) as a backbone network for object detection, proposing a ViT Detector that achieves performance competitive with traditional hierarchical backbones.
Baidu researchers introduce the PP-YOLOE object detector, which outperforms last year’s YOLOX in terms of speed and accuracy trade-off. The PP-YOLOE-l variant surpasses PP-YOLOv2 by 1.9 percent AP and YOLOX-l by 1.3 percent AP on COCO datasets.
NeurIPS 2020 Institute of Automation CAS and Microsoft Research Asia paper presents an attention-based decoder that integrates CV object representations
YOLOv4 is twice as fast as EfficientDet with comparable performance.
Researchers from Facebook AI and the University of Maryland have introduced a 21st century version — sweatshirts printed with adversarial examples that make the wearer undetectable to the AI-powered object detectors in today’s public surveillance systems.
Current state-of-the-art convolutional architectures for object detection tasks are human-designed. In a recent paper, Google Brain researchers leveraged the advantages of Neural Architecture Search (NAS) to propose NAS-FPN, a new automatic search method for feature pyramid architecture.
YOLO creators Joseph Redmon and Ali Farhadi from the University of Washington on March 25 released YOLOv3, an upgraded version of their fast object detection network, now available on Github.