Zobrazeno 1 - 10
of 15 429
pro vyhledávání: '"An, Junlin"'
Autor:
Su, Aofeng, Wang, Aowen, Ye, Chao, Zhou, Chen, Zhang, Ga, Chen, Gang, Zhu, Guangcheng, Wang, Haobo, Xu, Haokai, Chen, Hao, Li, Haoze, Lan, Haoxuan, Tian, Jiaming, Yuan, Jing, Zhao, Junbo, Zhou, Junlin, Shou, Kaizhe, Zha, Liangyu, Long, Lin, Li, Liyao, Wu, Pengzuo, Zhang, Qi, Huang, Qingyi, Yang, Saisai, Zhang, Tao, Ye, Wentao, Zhu, Wufang, Hu, Xiaomeng, Gu, Xijun, Sun, Xinjie, Li, Xiang, Yang, Yuhang, Xiao, Zhiqing
The emergence of models like GPTs, Claude, LLaMA, and Qwen has reshaped AI applications, presenting vast new opportunities across industries. Yet, the integration of tabular data remains notably underdeveloped, despite its foundational role in numero
Externí odkaz:
http://arxiv.org/abs/2411.02059
Autor:
Xiao, Fan, Hou, Junlin, Zhao, Ruiwei, Feng, Rui, Zou, Haidong, Lu, Lina, Xu, Yi, Zhang, Juzhao
Diabetic retinopathy (DR) is a leading cause of blindness worldwide and a common complication of diabetes. As two different imaging tools for DR grading, color fundus photography (CFP) and infrared fundus photography (IFP) are highly-correlated and c
Externí odkaz:
http://arxiv.org/abs/2411.00726
Self-supervised learning (SSL) has rapidly advanced in recent years, approaching the performance of its supervised counterparts through the extraction of representations from unlabeled data. However, dimensional collapse, where a few large eigenvalue
Externí odkaz:
http://arxiv.org/abs/2411.00392
Multi-View Representation Learning (MVRL) aims to learn a unified representation of an object from multi-view data. Deep Canonical Correlation Analysis (DCCA) and its variants share simple formulations and demonstrate state-of-the-art performance. Ho
Externí odkaz:
http://arxiv.org/abs/2411.00383
Autor:
Guo, Junlin, Lu, Siqi, Cui, Can, Deng, Ruining, Yao, Tianyuan, Tao, Zhewen, Lin, Yizhe, Lionts, Marilyn, Liu, Quan, Xiong, Juming, Wang, Yu, Zhao, Shilin, Chang, Catie, Wilkes, Mitchell, Yin, Mengmeng, Yang, Haichun, Huo, Yuankai
Training AI foundation models has emerged as a promising large-scale learning approach for addressing real-world healthcare challenges, including digital pathology. While many of these models have been developed for tasks like disease diagnosis and t
Externí odkaz:
http://arxiv.org/abs/2411.00078
Autor:
Jia, Chengyou, Luo, Minnan, Dang, Zhuohang, Sun, Qiushi, Xu, Fangzhi, Hu, Junlin, Xie, Tianbao, Wu, Zhiyong
Digital agents capable of automating complex computer tasks have attracted considerable attention due to their immense potential to enhance human-computer interaction. However, existing agent methods exhibit deficiencies in their generalization and s
Externí odkaz:
http://arxiv.org/abs/2410.18603
Models based on human-understandable concepts have received extensive attention to improve model interpretability for trustworthy artificial intelligence in the field of medical image analysis. These methods can provide convincing explanations for mo
Externí odkaz:
http://arxiv.org/abs/2410.15446
Autor:
Yang, Ling, Zhang, Zixiang, Han, Junlin, Zeng, Bohan, Li, Runjia, Torr, Philip, Zhang, Wentao
Generating high-quality 3D assets from textual descriptions remains a pivotal challenge in computer graphics and vision research. Due to the scarcity of 3D data, state-of-the-art approaches utilize pre-trained 2D diffusion priors, optimized through S
Externí odkaz:
http://arxiv.org/abs/2410.09009
Autor:
Du, Guodong, Lee, Junlin, Li, Jing, Jiang, Runhua, Guo, Yifei, Yu, Shuyang, Liu, Hanting, Goh, Sim Kuan, Tang, Ho-Kin, He, Daojing, Zhang, Min
While fine-tuning pretrained models has become common practice, these models often underperform outside their specific domains. Recently developed model merging techniques enable the direct integration of multiple models, each fine-tuned for distinct
Externí odkaz:
http://arxiv.org/abs/2410.02396
The increasing demand for transparent and reliable models, particularly in high-stakes decision-making areas such as medical image analysis, has led to the emergence of eXplainable Artificial Intelligence (XAI). Post-hoc XAI techniques, which aim to
Externí odkaz:
http://arxiv.org/abs/2410.02331