1 code implementation • 8 Aug 2023 • Yilun Chen, Zhiding Yu, Yukang Chen, Shiyi Lan, Animashree Anandkumar, Jiaya Jia, Jose Alvarez
For 3D object detection, we instantiate this method as FocalFormer3D, a simple yet effective detector that excels at excavating difficult objects and improving prediction recall.
Ranked #8 on 3D Object Detection on nuScenes
no code implementations • 25 Jun 2023 • Anna Bair, Hongxu Yin, Maying Shen, Pavlo Molchanov, Jose Alvarez
Robustness and compactness are two essential attributes of deep learning models that are deployed in the real world.
no code implementations • ICCV 2023 • Yanwei Li, Zhiding Yu, Jonah Philion, Anima Anandkumar, Sanja Fidler, Jiaya Jia, Jose Alvarez
In this work, we present an end-to-end framework for camera-based 3D multi-object tracking, called DQTrack.
1 code implementation • CVPR 2022 • Hongxu Yin, Arash Vahdat, Jose Alvarez, Arun Mallya, Jan Kautz, Pavlo Molchanov
A-ViT achieves this by automatically reducing the number of tokens in vision transformers that are processed in the network as inference proceeds.
Ranked #34 on Efficient ViTs on ImageNet-1K (with DeiT-S)
no code implementations • 17 Jun 2016 • Jose Alvarez, Lars Petersson
Deep learning and convolutional neural networks (ConvNets) have been successfully applied to most relevant tasks in the computer vision community.
no code implementations • 22 Oct 2014 • German Ros, Jose Alvarez, Julio Guerrero
To this end we propose the Robust Decomposition with Constrained Rank (RD-CR), a proximal gradient based method that enforces the rank constraints inherent to motion estimation.