Published in International Journal of Computer Vision
We present a visual imitation learning framework that enables learning of robot action policies solely based on expert samples without any robot trials. Robot exploration and on-policy trials in a real-world environment could often be expensive/dangerous. We present a new approach to address this problem by learning a future scene prediction model ...
Published in International Journal of Computer Vision
This work addresses the problem of semantic foggy scene understanding (SFSU). Although extensive research has been performed on image dehazing and on semantic scene understanding with clear-weather images, little attention has been paid to SFSU. Due to the difficulty of collecting and annotating foggy images, we choose to generate synthetic fog on ...
Published in International Journal of Computer Vision
Published in International Journal of Computer Vision
This paper addresses the problem of registering a known structured 3D scene, typically a 3D scan, and its metric Structure-from-Motion (SfM) counterpart. The proposed registration method relies on a prior plane segmentation of the 3D scan. Alignment is carried out by solving either the point-to-plane assignment problem, should the SfM reconstructio...
Published in International Journal of Computer Vision
Given an artistic portrait, recovering the latent photorealistic face that preserves the subject’s identity is challenging because the facial details are often distorted or fully lost in artistic portraits. We develop an Identity-preserving Face Recovery from Portraits method that utilizes a Style Removal network (SRN) and a Discriminative Network ...
Published in International Journal of Computer Vision
We identify two issues as key to developing effective face recognition systems: maximizing the appearance variations of training images and minimizing appearance variations in test images. The former is required to train the system for whatever appearance variations it will ultimately encounter and is often addressed by collecting massive training ...
Published in International Journal of Computer Vision
The large domain discrepancy between faces captured in polarimetric (or conventional) thermal and visible domains makes cross-domain face verification a highly challenging problem for human examiners as well as computer vision algorithms. Previous approaches utilize either a two-step procedure (visible feature estimation and visible image reconstru...
Published in International Journal of Computer Vision
Deep neural network (DNN) architecture based models have high expressive power and learning capacity. However, they are essentially a black box method since it is not easy to mathematically formulate the functions that are learned within its many layers of representation. Realizing this, many researchers have started to design methods to exploit th...
Published in International Journal of Computer Vision
Tracking the head in a video stream is a common thread seen within computer vision literature, supplying the research community with a large number of challenging and interesting problems. Head pose estimation from monocular cameras is often considered an extended application after the face tracking task has already been performed. This often invol...
Published in International Journal of Computer Vision
Deep generative models have significantly advanced image generation, enabling generation of visually pleasing images with realistic texture. Apart from the texture, it is the shape geometry of objects that strongly dictates their appearance. However, currently available generative models do not incorporate geometric information into the image gener...
