Semi-Supervised Recognition under a Noisy and Fine-grained Dataset

by   Cheng Cui, et al.

Simi-Supervised Recognition Challenge-FGVC7 is a challenging fine-grained recognition competition. One of the difficulties of this competition is how to use unlabeled data. We adopted pseudo-tag data mining to increase the amount of training data. The other one is how to identify similar birds with a very small difference, especially those have a relatively tiny main-body in examples. We combined generic image recognition and fine-grained image recognition method to solve the problem. All generic image recognition models were training using PaddleClas . Using the combination of two different ways of deep recognition models, we finally won the third place in the competition.


BiSTF: Bilateral-Branch Self-Training Framework for Semi-Supervised Large-scale Fine-Grained Recognition

Semi-supervised Fine-Grained Recognition is a challenge task due to the ...

Solution for Large-scale Long-tailed Recognition with Noisy Labels

This is a technical report for CVPR 2021 AliProducts Challenge. AliProdu...

Fine-Grained Adversarial Semi-supervised Learning

In this paper we exploit Semi-Supervised Learning (SSL) to increase the ...

PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Table Image Recognition to Latex

This paper presents our solution for the ICDAR 2021 Competition on Scien...

Fine-grained Hand Gesture Recognition in Multi-viewpoint Hand Hygiene

This paper contributes a new high-quality dataset for hand gesture recog...

Taxonomy and evolution predicting using deep learning in images

Molecular and morphological characters, as important parts of biological...

Tips and Tricks for Webly-Supervised Fine-Grained Recognition: Learning from the WebFG 2020 Challenge

WebFG 2020 is an international challenge hosted by Nanjing University of...