no code implementations • 7 Apr 2024 • Fanjie Kong, Yanbei Chen, Jiarui Cai, Davide Modolo
Specifically, we bootstrap dense synthetic captions using pre-trained VLMs to provide rich descriptions on different regions in images, and incorporate these captions to train a novel detector that generalizes to novel concepts.
no code implementations • 2 Feb 2023 • Fanjie Kong, Yuan Li, Houssam Nassif, Tanner Fiez, Ricardo Henao, Shreya Chakrabarti
In digital marketing, experimenting with new website content is one of the key levers to improve customer engagement.
1 code implementation • CVPR 2022 • Fanjie Kong, Ricardo Henao
Specifically, these classification tasks face two key challenges: $i$) the size of the input image is usually in the order of mega- or giga-pixels, however, existing deep architectures do not easily operate on such big images due to memory constraints, consequently, we seek a memory-efficient method to process these images; and $ii$) only a very small fraction of the input images are informative of the label of interest, resulting in low region of interest (ROI) to image ratio.
1 code implementation • 8 Jan 2021 • Fanjie Kong, Xiao-Yang Liu, Ricardo Henao
In the end, our experimental results indicate that tensor network models are effective for tiny object classification problem and potentially will beat state-of-the-art.
no code implementations • 6 Dec 2020 • Dong Wang, Yuewei Yang, Chenyang Tao, Zhe Gan, Liqun Chen, Fanjie Kong, Ricardo Henao, Lawrence Carin
Deep neural networks excel at comprehending complex visual signals, delivering on par or even superior performance to that of human experts.
1 code implementation • ICCV 2021 • Colin L. Cooke, Fanjie Kong, Amey Chaware, Kevin C. Zhou, Kanghyun Kim, Rong Xu, D. Michael Ando, Samuel J. Yang, Pavan Chandra Konda, Roarke Horstmeyer
This paper introduces a new method of data-driven microscope design for virtual fluorescence microscopy.
1 code implementation • 15 Jan 2020 • Fanjie Kong, Bohao Huang, Kyle Bradbury, Jordan M. Malof
Recently deep learning - namely convolutional neural networks (CNNs) - have yielded impressive performance for the task of building segmentation on large overhead (e. g., satellite) imagery benchmarks.