30 citations · 55 across the 5 of their papers we have counts for
5 papers
PartImageNet: A Large, High-Quality Dataset of Parts
Ju He, Shuo Yang, Shaokang Yang +7
It is natural to represent objects in terms of their parts. This has the potential to improve the performance of algorithms for object recognition and segmentation but can also hel…
3rd Place Solution to Google Landmark Recognition Competition 2021
Cheng Xu, Weimin Wang, Shuai Liu +6
In this paper, we show our solution to the Google Landmark Recognition 2021 Competition. Firstly, embeddings of images are extracted via various architectures (i.e. CNN-, Transform…
Rethinking Re-Sampling in Imbalanced Semi-Supervised Learning
Ju He, Adam Kortylewski, Shaokang Yang +4
Semi-Supervised Learning (SSL) has shown its strong ability in utilizing unlabeled data when labeled data is scarce. However, most SSL algorithms work under the assumption that the…
TransFG: A Transformer Architecture for Fine-grained Recognition
Ju He, Jie-Neng Chen, Shuai Liu +4
Fine-grained visual classification (FGVC) which aims at recognizing objects from subcategories is a very challenging task due to the inherently subtle inter-class differences. Most…
Re-rank Coarse Classification with Local Region Enhanced Features for Fine-Grained Image Recognition
Shaokang Yang, Shuai Liu, Cheng Yang +1
Fine-grained image recognition is very challenging due to the difficulty of capturing both semantic global features and discriminative local features. Meanwhile, these two features…