1 code implementation • 19 Jul 2024 • Yuetan Chu, Yilan Zhang, Zhongyi Han, Changchun Yang, Longxi Zhou, Gongning Luo, Xin Gao
Foundation models have recently attracted significant attention for their impressive generalizability across diverse downstream tasks.
1 code implementation • 3 Jan 2024 • Yilan Zhang, Yingxue Xu, Jianqi Chen, Fengying Xie, Hao Chen
Despite advantages of multimodal learning for cancer survival prediction, massive redundancy in multimodal data prevents it from extracting discriminative and compact information: (1) An extensive amount of intra-modal task-unrelated information blurs discriminability, especially for gigapixel whole slide images (WSIs) with many patches in pathology and thousands of pathways in genomic data, leading to an ``intra-modal redundancy" issue.
1 code implementation • 17 Jul 2023 • Jianqi Chen, Yilan Zhang, Zhengxia Zou, Keyan Chen, Zhenwei Shi
Initially, we employ a pretrained vision-language model (VLM) to generate descriptions for the composite image.
1 code implementation • 9 Jul 2023 • Yilan Zhang, Jianqi Chen, Ke Wang, Fengying Xie
In this paper, we propose class-Enhancement Contrastive Learning (ECL), which enriches the information of minority classes and treats different classes equally.
1 code implementation • 14 May 2023 • Jianqi Chen, Hao Chen, Keyan Chen, Yilan Zhang, Zhengxia Zou, Zhenwei Shi
Many existing adversarial attacks generate $L_p$-norm perturbations on image RGB space.
1 code implementation • 3 Mar 2023 • Jianqi Chen, Yilan Zhang, Zhengxia Zou, Keyan Chen, Zhenwei Shi
High-resolution (HR) image harmonization is of great significance in real-world applications such as image synthesis and image editing.
Ranked #8 on Image Harmonization on iHarmony4
1 code implementation • 21 Nov 2022 • Yilan Zhang, Fengying Xie, Jianqi Chen
Multi-modal skin lesion diagnosis (MSLD) has achieved remarkable success by modern computer-aided diagnosis (CAD) technology based on deep convolutions.
1 code implementation • 1 Aug 2022 • Yilan Zhang, Fengying Xie, Xuedong Song, Hangning Zhou, Yiguang Yang, Haopeng Zhang, Jie Liu
As such they have made great improvements in many tasks of dermoscopy images.