Deep learning (DL) is revolutionizing evidence-based decision-making techniques that can
be applied across various sectors. Specifically, it possesses the ability to utilize two or more …

Deep learning algorithms are a subset of the machine learning algorithms, which aim at
discovering multiple levels of distributed representations. Recently, numerous deep learning …

Incorporation of prior knowledge about organ shape and location is key to improve
performance of image analysis approaches. In particular, priors can be useful in cases …

In this paper we introduce a generative model capable of producing high quality samples of
natural images. Our approach uses a cascade of convolutional networks (convnets) within a …

In this paper, we propose a generative model, Temporal Generative Adversarial Nets
(TGAN), which can learn a semantic representation of unlabeled videos, and is capable of …

Is strong supervision necessary for learning a good visual representation? Do we really
need millions of semantically-labeled images to train a Convolutional Neural Network …

Abstract 3D shape is a crucial but heavily underutilized cue in today's computer vision
systems, mostly due to the lack of a good generic shape representation. With the recent …

The current dominant paradigm for feature learning in computer vision relies on training
neural networks for the task of object recognition using millions of hand labelled images. Is it …

摘要基于视觉的目标检测与跟踪是图像处理, 计算机视觉, 模式识别等众多学科的交叉研究课题,
在视频监控, 虚拟现实, 人机交互, 自主导航等领域, 具有重要的理论研究意义和实际应用价值 …

While convolutional neural networks have gained impressive success recently in solving
structured prediction problems such as semantic segmentation, it remains a challenge to …