Detecting and segmenting salient objects from natural scenes, often referred to as salient
object detection, has attracted great interest in computer vision. While many models have …

Colorization is a process of converting grayscale images into visually acceptable color
images. The main goal is to convince the viewer of the authenticity of the result. Grayscale …

Do visual tasks have a relationship, or are they unrelated? For instance, could having
surface normals simplify estimating the depth of an image? Intuition answers these …

This paper tackles the automatic colorization task of a sketch image given an already-
colored reference image. Colorizing a sketch image is in high demand in comics, animation …

We use large amounts of unlabeled video to learn models for visual tracking without manual
human supervision. We leverage the natural temporal coherency of color to create a model …

We propose a deep learning approach for user-guided image colorization. The system
directly maps a grayscale image, along with sparse, local user" hints" to an output …

Given a grayscale photograph as input, this paper attacks the problem of hallucinating a
plausible color version of the photograph. This problem is clearly underconstrained, so …

We present a novel technique to automatically colorize grayscale images that combines
both global priors and local image features. Based on Convolutional Neural Networks, our …

We propose the first deep learning approach for exemplar-based local colorization. Given a
reference color image, our convolutional neural network directly maps a grayscale image to …

Image colorization is a challenging problem due to multi-modal uncertainty and high ill-
posedness. Directly training a deep neural network usually leads to incorrect semantic …