Zobrazeno 1 - 10
of 475
pro vyhledávání: '"Metaxas, Dimitris N."'
Autor:
Jin, Can, Peng, Hongwu, Zhao, Shiyu, Wang, Zhenting, Xu, Wujiang, Han, Ligong, Zhao, Jiahui, Zhong, Kai, Rajasekaran, Sanguthevar, Metaxas, Dimitris N.
Large Language Models (LLMs) have significantly enhanced Information Retrieval (IR) across various modules, such as reranking. Despite impressive performance, current zero-shot relevance ranking with LLMs heavily relies on human prompt engineering. E
Externí odkaz:
http://arxiv.org/abs/2406.14449
Autor:
Zhangli, Qilong, Jiang, Jindong, Liu, Di, Yu, Licheng, Dai, Xiaoliang, Ramchandani, Ankit, Pang, Guan, Metaxas, Dimitris N., Krishnan, Praveen
Publikováno v:
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 7496-7506
While diffusion models have significantly advanced the quality of image generation their capability to accurately and coherently render text within these images remains a substantial challenge. Conventional diffusion-based methods for scene text gene
Externí odkaz:
http://arxiv.org/abs/2406.01062
Autor:
Zhang, Xinxi, Wen, Song, Han, Ligong, Juefei-Xu, Felix, Srivastava, Akash, Huang, Junzhou, Wang, Hao, Tao, Molei, Metaxas, Dimitris N.
Adapting large-scale pre-trained generative models in a parameter-efficient manner is gaining traction. Traditional methods like low rank adaptation achieve parameter efficiency by imposing constraints but may not be optimal for tasks requiring high
Externí odkaz:
http://arxiv.org/abs/2405.21050
Autor:
Li, Zhuowei, Xu, Zihao, Han, Ligong, Gao, Yunhe, Wen, Song, Liu, Di, Wang, Hao, Metaxas, Dimitris N.
In-context Learning (ICL) empowers large language models (LLMs) to adapt to unseen tasks during inference by prefixing a few demonstration examples prior to test queries. Despite its versatility, ICL incurs substantial computational and memory overhe
Externí odkaz:
http://arxiv.org/abs/2405.14660
Latent generative models (e.g., Stable Diffusion) have become more and more popular, but concerns have arisen regarding potential misuse related to images generated by these models. It is, therefore, necessary to analyze the origin of images by infer
Externí odkaz:
http://arxiv.org/abs/2405.13360
The perception of 3D motion of surrounding traffic participants is crucial for driving safety. While existing works primarily focus on general large motions, we contend that the instantaneous detection and quantification of subtle motions is equally
Externí odkaz:
http://arxiv.org/abs/2405.02781
Autor:
Lyu, Jun, Qin, Chen, Wang, Shuo, Wang, Fanwen, Li, Yan, Wang, Zi, Guo, Kunyuan, Ouyang, Cheng, Tänzer, Michael, Liu, Meng, Sun, Longyu, Sun, Mengting, Li, Qin, Shi, Zhang, Hua, Sha, Li, Hao, Chen, Zhensen, Zhang, Zhenlin, Xin, Bingyu, Metaxas, Dimitris N., Yiasemis, George, Teuwen, Jonas, Zhang, Liping, Chen, Weitian, Zhao, Yidong, Tao, Qian, Pang, Yanwei, Liu, Xiaohan, Razumov, Artem, Dylov, Dmitry V., Dou, Quan, Yan, Kang, Xue, Yuyang, Du, Yuning, Dietlmeier, Julia, Garcia-Cabrera, Carles, Hemidi, Ziad Al-Haj, Vogt, Nora, Xu, Ziqiang, Zhang, Yajing, Chu, Ying-Hua, Chen, Weibo, Bai, Wenjia, Zhuang, Xiahai, Qin, Jing, Wu, Lianmin, Yang, Guang, Qu, Xiaobo, Wang, He, Wang, Chengyan
Cardiac MRI, crucial for evaluating heart structure and function, faces limitations like slow imaging and motion artifacts. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and e
Externí odkaz:
http://arxiv.org/abs/2404.01082
Generalization remains a central challenge in machine learning. In this work, we propose Learning from Teaching (LoT), a novel regularization technique for deep neural networks to enhance generalization. Inspired by the human ability to capture conci
Externí odkaz:
http://arxiv.org/abs/2402.02769
Autor:
Zhao, Shiyu, Zhao, Long, G, Vijay Kumar B., Suh, Yumin, Metaxas, Dimitris N., Chandraker, Manmohan, Schulter, Samuel
The recent progress in language-based open-vocabulary object detection can be largely attributed to finding better ways of leveraging large-scale data with free-form text annotations. Training such models with a discriminative objective function has
Externí odkaz:
http://arxiv.org/abs/2401.00094
The key to dynamic or multi-contrast magnetic resonance imaging (MRI) reconstruction lies in exploring inter-frame or inter-contrast information. Currently, the unrolled model, an approach combining iterative MRI reconstruction steps with learnable n
Externí odkaz:
http://arxiv.org/abs/2309.13839