2 code implementations • ICCV 2021 • Yinxiao Li, Pengchong Jin, Feng Yang, Ce Liu, Ming-Hsuan Yang, Peyman Milanfar
Most video super-resolution methods focus on restoring high-resolution video frames from low-resolution videos without taking into account compression.
no code implementations • ECCV 2020 • Xianzhi Du, Tsung-Yi Lin, Pengchong Jin, Yin Cui, Mingxing Tan, Quoc Le, Xiaodan Song
Furthermore, SpineNet is built with a uniform resource distribution over operations.
1 code implementation • ECCV 2020 • Jiahui Yu, Pengchong Jin, Hanxiao Liu, Gabriel Bender, Pieter-Jan Kindermans, Mingxing Tan, Thomas Huang, Xiaodan Song, Ruoming Pang, Quoc Le
Without extra retraining or post-processing steps, we are able to train a single set of shared weights on ImageNet and use these weights to obtain child models whose sizes range from 200 to 1000 MFLOPs.
Ranked #30 on Neural Architecture Search on ImageNet
13 code implementations • CVPR 2020 • Xianzhi Du, Tsung-Yi Lin, Pengchong Jin, Golnaz Ghiasi, Mingxing Tan, Yin Cui, Quoc V. Le, Xiaodan Song
We propose SpineNet, a backbone with scale-permuted intermediate features and cross-scale connections that is learned on an object detection task by Neural Architecture Search.
Ranked #9 on Image Classification on iNaturalist
no code implementations • 25 Sep 2019 • Jiahui Yu, Pengchong Jin, Hanxiao Liu, Gabriel Bender, Pieter-Jan Kindermans, Mingxing Tan, Thomas Huang, Xiaodan Song, Quoc Le
In this work, we propose BigNAS, an approach that simplifies this workflow and scales up neural architecture search to target a wide range of model sizes simultaneously.
2 code implementations • 9 Jul 2018 • Pengchong Jin, Vivek Rathod, Xiangxin Zhu
We share box predictors across all scales, and replace convolution between scales with max pooling.