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20112023
most citedTransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

4k citations · 16.1k across the 265 of their papers we have counts for

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Showing 2022 · cs.CVShow all

22 papers · 2 filters

cs.CV2022★ 2 cited

AsyInst: Asymmetric Affinity with DepthGrad and Color for Box-Supervised Instance Segmentation

Siwei Yang, Longlong Jing, Junfei Xiao +3

The weakly supervised instance segmentation is a challenging task. The existing methods typically use bounding boxes as supervision and optimize the network with a regularization l…

cs.CV2022★ 13 cited

Unleashing the Power of Visual Prompting At the Pixel Level

Junyang Wu, Xianhang Li, Chen Wei +4

This paper presents a simple and effective visual prompting method for adapting pre-trained models to downstream recognition tasks. Our method includes two key designs. First, rath…

cs.CV2022★ 4 cited

SMAUG: Sparse Masked Autoencoder for Efficient Video-Language Pre-training

Yuanze Lin, Chen Wei, Huiyu Wang +2

Video-language pre-training is crucial for learning powerful multi-modal representation. However, it typically requires a massive amount of computation. In this paper, we develop S…

cs.CV2022★ 3 cited

LUMix: Improving Mixup by Better Modelling Label Uncertainty

Shuyang Sun, Jie-Neng Chen, Ruifei He +3

Modern deep networks can be better generalized when trained with noisy samples and regularization techniques. Mixup and CutMix have been proven to be effective for data augmentatio…

cs.CV2022★ 2 cited

1st Place Solution of The Robust Vision Challenge 2022 Semantic Segmentation Track

Junfei Xiao, Zhichao Xu, Shiyi Lan +3

This report describes the winning solution to the Robust Vision Challenge (RVC) semantic segmentation track at ECCV 2022. Our method adopts the FAN-B-Hybrid model as the encoder an…

cs.CV2022★ 6 cited

Delving into Masked Autoencoders for Multi-Label Thorax Disease Classification

Junfei Xiao, Yutong Bai, Alan Yuille +1

Vision Transformer (ViT) has become one of the most popular neural architectures due to its great scalability, computational efficiency, and compelling performance in many vision t…