Goyal, Yash Khot, Tejas Agrawal, Aishwarya Summers-Stay, Douglas Batra, Dhruv Parikh, Devi
Published in
International Journal of Computer Vision
The problem of visual question answering (VQA) is of significant importance both as a challenging research question and for the rich set of applications it enables. In this context, however, inherent structure in our world and bias in our language tend to be a simpler signal for learning than visual modalities, resulting in VQA models that ignore v...
Gilbert, Andrew Trumble, Matthew Malleson, Charles Hilton, Adrian Collomosse, John
Published in
International Journal of Computer Vision
We propose an approach to accurately estimate 3D human pose by fusing multi-viewpoint video (MVV) with inertial measurement unit (IMU) sensor data, without optical markers, a complex hardware setup or a full body model. Uniquely we use a multi-channel 3D convolutional neural network to learn a pose embedding from visual occupancy and semantic 2D po...
Rogez, Grégory Schmid, Cordelia
Published in
International Journal of Computer Vision
This paper addresses the problem of 3D human pose estimation in the wild. A significant challenge is the lack of training data, i.e., 2D images of humans annotated with 3D poses. Such data is necessary to train state-of-the-art CNN architectures. Here, we propose a solution to generate a large set of photorealistic synthetic images of humans with 3...
Duong, Chi Nhan Luu, Khoa Quach, Kha Gia Bui, Tien D.
Published in
International Journal of Computer Vision
The “interpretation through synthesis” approach to analyze face images, particularly Active Appearance Models (AAMs) method, has become one of the most successful face modeling approaches over the last two decades. AAM models have ability to represent face images through synthesis using a controllable parameterized Principal Component Analysis (PCA...
Zhang, Dingwen Han, Junwei Zhao, Long Meng, Deyu
Published in
International Journal of Computer Vision
Weakly supervised object detection is an interesting yet challenging research topic in computer vision community, which aims at learning object models to localize and detect the corresponding objects of interest only under the supervision of image-level annotation. For addressing this problem, this paper establishes a novel weakly supervised learni...
Cherian, Anoop Gould, Stephen
Published in
International Journal of Computer Vision
Deep learning models for video-based action recognition usually generate features for short clips (consisting of a few frames); such clip-level features are aggregated to video-level representations by computing statistics on these features. Typically zero-th (max) or the first-order (average) statistics are used. In this paper, we explore the bene...
Ruder, Manuel Dosovitskiy, Alexey Brox, Thomas
Published in
International Journal of Computer Vision
Manually re-drawing an image in a certain artistic style takes a professional artist a long time. Doing this for a video sequence single-handedly is beyond imagination. We present two computational approaches that transfer the style from one image (for example, a painting) to a whole video sequence. In our first approach, we adapt to videos the ori...
Wang, Hanxiao Zhu, Xiatian Gong, Shaogang Xiang, Tao
Published in
International Journal of Computer Vision
Most existing person re-identification (re-id) methods are unsuitable for real-world deployment due to two reasons: Unscalability to large population size, and Inadaptability over time. In this work, we present a unified solution to address both problems. Specifically, we propose to construct an identity regression space (IRS) based on embedding di...
Yang, Yingzhen Feng, Jiashi Jojic, Nebojsa Yang, Jianchao Huang, Thomas S.
Published in
International Journal of Computer Vision
Subspace clustering methods partition the data that lie in or close to a union of subspaces in accordance with the subspace structure. Such methods with sparsity prior, such as sparse subspace clustering (SSC) (Elhamifar and Vidal in IEEE Trans Pattern Anal Mach Intell 35(11):2765–2781, 2013) with the sparsity induced by the ℓ1\documentclass[12pt]{...
Owens, Andrew Wu, Jiajun McDermott, Josh H. Freeman, William T. Torralba, Antonio
Published in
International Journal of Computer Vision
The sound of crashing waves, the roar of fast-moving cars—sound conveys important information about the objects in our surroundings. In this work, we show that ambient sounds can be used as a supervisory signal for learning visual models. To demonstrate this, we train a convolutional neural network to predict a statistical summary of the sound asso...
