Recent advances in deep learning techniques have led to improved diagnostic abilities in ophthalmology. A generative adversarial network (GAN), which consists of two competing types of deep neural networks, including a generator and a discriminator, has
Deep neural networks are the driving force of the recent explosion of machine learning applications in everyday life. However, they usually require a lot o
Extracting useful features at multiple scales is a crucial task in computer vision. The emergence of deep-learning techniques and the advancements in convolutional neural networks (CNNs) have facilitated effective multiscale feature extraction that resul
Single image dehazing, as a key prerequisite of high-level computer vision tasks, catches more and more attentions. Traditional model-based methods recover haze-free images via atmospheric scattering model, which achieve favorable dehazing effect but endure artifacts, halos, and color distortion. By ...
Since 1990, he spent 10 years to complete his series "Surround the City", which is one of the most magnificent photo documents in 90's of Shenzhen. Critic Gu Zheng wrote "'Surround the City' unfolds the most remarkable event in China's social changes: farmers leave the farms and come ...
With the advent of advancements in deep learning approaches, such as deep convolution neural network, residual neural network, adversarial network; U-Net a
Weixi Feng, Xuehai He, Tsu-Jui Fu, Varun Jampani, Arjun Akula, Pradyumna Narayana, Sugato Basu, Xin Eric Wang, William Yang Wang. arXiv 2022. [PDF]🧁In-Context GenerationAnalogist: Out-of-the-box Visual In-Context Learning with Image Diffusion Model. Zheng Gu, Shiyuan Yang, Jing Lia...
202304S. He et al.Accuracy of Segment-Anything Model (SAM) in medical image segmentation tasks(paper)None 202304T. Chen et al.SAM Fails to Segment Anything? – SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More(paper)Code ...
Recent progress in encoder–decoder neural network architecture design has led to significant performance improvements in a wide range of medical image segmentation tasks. However, state-of-the-art networks for a given task may be too computationally dem
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