Zobrazeno 1 - 10
of 5 504
pro vyhledávání: '"Kuo, C C"'
Human-Object Interaction (HOI) detection is a fundamental task in image understanding. While deep-learning-based HOI methods provide high performance in terms of mean Average Precision (mAP), they are computationally expensive and opaque in training
Externí odkaz:
http://arxiv.org/abs/2408.07018
As a fundamental task in natural language processing, word embedding converts each word into a representation in a vector space. A challenge with word embedding is that as the vocabulary grows, the vector space's dimension increases, which can lead t
Externí odkaz:
http://arxiv.org/abs/2407.12342
Blind Image Quality Assessment (BIQA) is an essential task that estimates the perceptual quality of images without reference. While many BIQA methods employ deep neural networks (DNNs) and incorporate saliency detectors to enhance performance, their
Externí odkaz:
http://arxiv.org/abs/2407.05590
We introduce GreenCOD, a green method for detecting camouflaged objects, distinct in its avoidance of backpropagation techniques. GreenCOD leverages gradient boosting and deep features extracted from pre-trained Deep Neural Networks (DNNs). Tradition
Externí odkaz:
http://arxiv.org/abs/2405.16144
Image saliency detection is crucial in understanding human gaze patterns from visual stimuli. The escalating demand for research in image saliency detection is driven by the growing necessity to incorporate such techniques into various computer visio
Externí odkaz:
http://arxiv.org/abs/2404.00253
Autor:
Yang, Yijing, Magoulianitis, Vasileios, Yang, Jiaxin, Xue, Jintang, Kaneko, Masatomo, Cacciamani, Giovanni, Abreu, Andre, Duddalwar, Vinay, Kuo, C. -C. Jay, Gill, Inderbir S., Nikias, Chrysostomos
Automatic prostate segmentation is an important step in computer-aided diagnosis of prostate cancer and treatment planning. Existing methods of prostate segmentation are based on deep learning models which have a large size and lack of transparency w
Externí odkaz:
http://arxiv.org/abs/2403.15971
Autor:
Magoulianitis, Vasileios, Yang, Jiaxin, Yang, Yijing, Xue, Jintang, Kaneko, Masatomo, Cacciamani, Giovanni, Abreu, Andre, Duddalwar, Vinay, Kuo, C. -C. Jay, Gill, Inderbir S., Nikias, Chrysostomos
Prostate Cancer is one of the most frequently occurring cancers in men, with a low survival rate if not early diagnosed. PI-RADS reading has a high false positive rate, thus increasing the diagnostic incurred costs and patient discomfort. Deep learni
Externí odkaz:
http://arxiv.org/abs/2403.15969
Autor:
Liu, Xiaofeng, Shusharina, Nadya, Shih, Helen A, Kuo, C. -C. Jay, Fakhri, Georges El, Woo, Jonghye
In this work, we aim to predict the survival time (ST) of glioblastoma (GBM) patients undergoing different treatments based on preoperative magnetic resonance (MR) scans. The personalized and precise treatment planning can be achieved by comparing th
Externí odkaz:
http://arxiv.org/abs/2402.06982
AI algorithms at the edge demand smaller model sizes and lower computational complexity. To achieve these objectives, we adopt a green learning (GL) paradigm rather than the deep learning paradigm. GL has three modules: 1) unsupervised representation
Externí odkaz:
http://arxiv.org/abs/2312.14968
Unsupervised image-to-image (I2I) translation learns cross-domain image mapping that transfers input from the source domain to output in the target domain while preserving its semantics. One challenge is that different semantic statistics in source a
Externí odkaz:
http://arxiv.org/abs/2310.04995