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

21 citations · 40 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV20222 cited

Exploring Stochastic Autoregressive Image Modeling for Visual Representation

Yu Qi, Fan Yang, Yousong Zhu +4

Autoregressive language modeling (ALM) have been successfully used in self-supervised pre-training in Natural language processing (NLP). However, this paradigm has not achieved com…

cs.CV20222 cited

MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly Detection

Tianpeng Bao, Jiadong Chen, Wei Li +5

Visual anomaly detection plays a crucial role in not only manufacturing inspection to find defects of products during manufacturing processes, but also maintenance inspection to ke…

cs.CV20228 cited

Obj2Seq: Formatting Objects as Sequences with Class Prompt for Visual Tasks

Zhiyang Chen, Yousong Zhu, Zhaowen Li +8

Visual tasks vary a lot in their output formats and concerned contents, therefore it is hard to process them with an identical structure. One main obstacle lies in the high-dimensi…

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.CV20222 cited

Uni6D: A Unified CNN Framework without Projection Breakdown for 6D Pose Estimation

Xiaoke Jiang, Donghai Li, Hao Chen +3

As RGB-D sensors become more affordable, using RGB-D images to obtain high-accuracy 6D pose estimation results becomes a better option. State-of-the-art approaches typically use di…

cs.CV2022

UniVIP: A Unified Framework for Self-Supervised Visual Pre-training

Zhaowen Li, Yousong Zhu, Fan Yang +9

Self-supervised learning (SSL) holds promise in leveraging large amounts of unlabeled data. However, the success of popular SSL methods has limited on single-centric-object images…