15 citations · 25 across the 6 of their papers we have counts for
6 papers
With Greater Distance Comes Worse Performance: On the Perspective of Layer Utilization and Model Generalization
James Wang, Cheng-Lin Yang
Generalization of deep neural networks remains one of the main open problems in machine learning. Previous theoretical works focused on deriving tight bounds of model complexity, w…
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…
Incorporating Vision Bias into Click Models for Image-oriented Search Engine
Ningxin Xu, Cheng Yang, Yixin Zhu +2
Most typical click models assume that the probability of a document to be examined by users only depends on position, such as PBM and UBM. It works well in various kinds of search…