5 accepted papers
"This paper introduces an approach to linearly controllable generative adversarial networks (LC-GAN) driven by unsupervised learning. Departing from traditional methods relying on supervision signals or post-processing for latent feature disentanglement, our proposed technique enables unsupervised l…
As the quality of optical sensors improves, there is a need for processing large-scale images. In particular, the ability of devices to capture ultra-high definition (UHD) images and video places new demands on the image processing pipeline. In this paper, we consider the task of low-light image enh…
The Facial Action Coding System is a taxonomy for fine-grained facial expression analysis. This paper proposes a method for detecting Facial Action Units (FAU), which define particular face muscle activity, from an input image. FAU detection is formulated as a multi-task learning problem, where imag…
Existing deep learning methods for image deblurring typically train models using pairs of sharp images and their blurred counterparts. However, synthetically blurring images does not necessarily model the blurring process in real-world scenarios with sufficient accuracy. To address this problem, we…
In this paper we introduce a large-scale hand pose dataset, collected using a novel capture method. Existing datasets are either generated synthetically or captured using depth sensors: synthetic datasets exhibit a certain level of appearance difference from real depth images, and real datasets are…