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pro vyhledávání: '"Elsaddik, Abdulmotaleb"'
Autor:
Li, Mingjie, Lin, Haokun, Qiu, Liang, Liang, Xiaodan, Chen, Ling, Elsaddik, Abdulmotaleb, Chang, Xiaojun
Due to the common content of anatomy, radiology images with their corresponding reports exhibit high similarity. Such inherent data bias can predispose automatic report generation models to learn entangled and spurious representations resulting in mi
Externí odkaz:
http://arxiv.org/abs/2407.14474
Large Language Models (LLMs) have revolutionized various industries by harnessing their power to improve productivity and facilitate learning across different fields. One intriguing application involves combining LLMs with visual models to create a n
Externí odkaz:
http://arxiv.org/abs/2310.02739
In this paper, we introduce Divide-and-Conquer into the salient object detection (SOD) task to enable the model to learn prior knowledge that is for predicting the saliency map. We design a novel network, Divide-and-Conquer Network (DC-Net) which use
Externí odkaz:
http://arxiv.org/abs/2305.14955
Salient Object Detection (SOD) is a popular and important topic aimed at precise detection and segmentation of the interesting regions in the images. We integrate the linguistic information into the vision-based U-Structure networks designed for sali
Externí odkaz:
http://arxiv.org/abs/2208.04361
Publikováno v:
In Pattern Recognition January 2025 157
Akademický článek
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Publikováno v:
Signal Processing & Information Technology; 2012, p16-20, 5p
Publikováno v:
2012 International Conference on Information Technology Based Higher Education & Training (ITHET); 1/ 1/2012, p1-5, 5p