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20192022
most citedLearning from Future: A Novel Self-Training Framework for Semantic Segmentation

21 citations · 50 across the 7 of their papers we have counts for

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8 papers · 1 filter

cs.CV20225 cited

Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generation

Haochen Wang, Xiaodan Du, Jiahao Li +2

A diffusion model learns to predict a vector field of gradients. We propose to apply chain rule on the learned gradients, and back-propagate the score of a diffusion model through…

cs.CV202221 cited

Learning from Future: A Novel Self-Training Framework for Semantic Segmentation

Ye Du, Yujun Shen, Haochen Wang +6

Self-training has shown great potential in semi-supervised learning. Its core idea is to use the model learned on labeled data to generate pseudo-labels for unlabeled samples, and…

cs.CV202215 cited

NFormer: Robust Person Re-identification with Neighbor Transformer

Haochen Wang, Jiayi Shen, Yongtuo Liu +2

Person re-identification aims to retrieve persons in highly varying settings across different cameras and scenarios, in which robust and discriminative representation learning is c…

cs.CV20225 cited

Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels

Yuchao Wang, Haochen Wang, Yujun Shen +6

The crux of semi-supervised semantic segmentation is to assign adequate pseudo-labels to the pixels of unlabeled images. A common practice is to select the highly confident predict…

cs.CV20222 cited

Decoupled IoU Regression for Object Detection

Yan Gao, Qimeng Wang, Xu Tang +4

Non-maximum suppression (NMS) is widely used in object detection pipelines for removing duplicated bounding boxes. The inconsistency between the confidence for NMS and the real loc…

cs.CV20212 cited

SwiftNet: Real-time Video Object Segmentation

Haochen Wang, Xiaolong Jiang, Haibing Ren +2

In this work we present SwiftNet for real-time semisupervised video object segmentation (one-shot VOS), which reports 77.8% J &F and 70 FPS on DAVIS 2017 validation dataset, leadin…