481 citations · 1.2k across the 21 of their papers we have counts for
4 papers · 1 filter
Omni-Dimensional Dynamic Convolution
Chao Li, Aojun Zhou, Anbang Yao
Learning a single static convolutional kernel in each convolutional layer is the common training paradigm of modern Convolutional Neural Networks (CNNs). Instead, recent research i…
An Algorithm-Hardware Co-Optimized Framework for Accelerating N:M Sparse Transformers
Chao Fang, Aojun Zhou, Zhongfeng Wang
The Transformer has been an indispensable staple in deep learning. However, for real-life applications, it is very challenging to deploy efficient Transformers due to immense param…
Group R-CNN for Weakly Semi-supervised Object Detection with Points
Shilong Zhang, Zhuoran Yu, Liyang Liu +3
We study the problem of weakly semi-supervised object detection with points (WSSOD-P), where the training data is combined by a small set of fully annotated images with bounding bo…
Pyramid Fusion Transformer for Semantic Segmentation
Zipeng Qin, Jianbo Liu, Xiaolin Zhang +4
The recently proposed MaskFormer gives a refreshed perspective on the task of semantic segmentation: it shifts from the popular pixel-level classification paradigm to a mask-level…