10.5k citations · 11.5k across the 5 of their papers we have counts for
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Depth Pro: Sharp Monocular Metric Depth in Less Than a Second
Aleksei Bochkovskii, Amaël Delaunoy, Hugo Germain +4
We present a foundation model for zero-shot metric monocular depth estimation. Our model, Depth Pro, synthesizes high-resolution depth maps with unparalleled sharpness and high-fre…
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
Chien-Yao Wang, Alexey Bochkovskiy, Hong-Yuan Mark Liao
YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56.8% AP among all known real-time object det…
Non-deep Networks
Ankit Goyal, Alexey Bochkovskiy, Jia Deng +1
Depth is the hallmark of deep neural networks. But more depth means more sequential computation and higher latency. This begs the question -- is it possible to build high-performin…
Vision Transformers for Dense Prediction
René Ranftl, Alexey Bochkovskiy, Vladlen Koltun
We introduce dense vision transformers, an architecture that leverages vision transformers in place of convolutional networks as a backbone for dense prediction tasks. We assemble…
Scaled-YOLOv4: Scaling Cross Stage Partial Network
Chien-Yao Wang, Alexey Bochkovskiy, Hong-Yuan Mark Liao
We show that the YOLOv4 object detection neural network based on the CSP approach, scales both up and down and is applicable to small and large networks while maintaining optimal s…
YOLOv4: Optimal Speed and Accuracy of Object Detection
Alexey Bochkovskiy, Chien-Yao Wang, Hong-Yuan Mark Liao
There are a huge number of features which are said to improve Convolutional Neural Network (CNN) accuracy. Practical testing of combinations of such features on large datasets, and…