30 citations · 51 across the 6 of their papers we have counts for
6 papers
Mitigating Test-Time Bias for Fair Image Retrieval
Fanjie Kong, Shuai Yuan, Weituo Hao +1
We address the challenge of generating fair and unbiased image retrieval results given neutral textual queries (with no explicit gender or race connotations), while maintaining the…
Efficient Classification of Very Large Images with Tiny Objects
Fanjie Kong, Ricardo Henao
An increasing number of applications in computer vision, specially, in medical imaging and remote sensing, become challenging when the goal is to classify very large images with ti…
Quantum Tensor Network in Machine Learning: An Application to Tiny Object Classification
Fanjie Kong, Xiao-yang Liu, Ricardo Henao
Tiny object classification problem exists in many machine learning applications like medical imaging or remote sensing, where the object of interest usually occupies a small region…
Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models
Dong Wang, Yuewei Yang, Chenyang Tao +5
Deep neural networks excel at comprehending complex visual signals, delivering on par or even superior performance to that of human experts. However, ad-hoc visual explanations of…
Physics-enhanced machine learning for virtual fluorescence microscopy
Colin L. Cooke, Fanjie Kong, Amey Chaware +7
This paper introduces a new method of data-driven microscope design for virtual fluorescence microscopy. Our results show that by including a model of illumination within the first…
The Synthinel-1 dataset: a collection of high resolution synthetic overhead imagery for building segmentation
Fanjie Kong, Bohao Huang, Kyle Bradbury +1
Recently deep learning - namely convolutional neural networks (CNNs) - have yielded impressive performance for the task of building segmentation on large overhead (e.g., satellite)…