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20152023
most citedDelving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

1k citations · 1.4k across the 40 of their papers we have counts for

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30 papers · 1 filter

cs.CV2022

Progressive End-to-End Object Detection in Crowded Scenes

Anlin Zheng, Yuang Zhang, Xiangyu Zhang +2

In this paper, we propose a new query-based detection framework for crowd detection. Previous query-based detectors suffer from two drawbacks: first, multiple predictions will be i…

cs.CV202216 cited

Focal Sparse Convolutional Networks for 3D Object Detection

Yukang Chen, Yanwei Li, Xiangyu Zhang +2

Non-uniformed 3D sparse data, e.g., point clouds or voxels in different spatial positions, make contribution to the task of 3D object detection in different ways. Existing basic co…

cs.CV202281 cited

Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs

Xiaohan Ding, Xiangyu Zhang, Yizhuang Zhou +3

We revisit large kernel design in modern convolutional neural networks (CNNs). Inspired by recent advances in vision transformers (ViTs), in this paper, we demonstrate that using a…

cs.CV20224 cited

Tree Energy Loss: Towards Sparsely Annotated Semantic Segmentation

Zhiyuan Liang, Tiancai Wang, Xiangyu Zhang +2

Sparsely annotated semantic segmentation (SASS) aims to train a segmentation network with coarse-grained (i.e., point-, scribble-, and block-wise) supervisions, where only a small…

cs.CV2021

Partial to Whole Knowledge Distillation: Progressive Distilling Decomposed Knowledge Boosts Student Better

Xuanyang Zhang, Xiangyu Zhang, Jian Sun

Knowledge distillation field delicately designs various types of knowledge to shrink the performance gap between compact student and large-scale teacher. These existing distillatio…

cs.CV20211 cited

Image Synthesis via Semantic Composition

Yi Wang, Lu Qi, Ying-Cong Chen +2

In this paper, we present a novel approach to synthesize realistic images based on their semantic layouts. It hypothesizes that for objects with similar appearance, they share simi…