Research Area
Year Published

203 Results

October 25, 2019

Mesh R-CNN

International Conference on Computer Vision (ICCV)

We propose a system that detects objects in real-world images and produces a triangle mesh giving the full 3D shape of each detected object. Our system, called Mesh R-CNN, augments Mask R-CNN with a mesh prediction branch that outputs meshes with varying topological structure by first predicting coarse voxel representations which are converted to meshes and refined with a graph convolution network operating over the mesh’s vertices and edges.

By: Georgia Gkioxari, Jitendra Malik, Justin Johnson

October 25, 2019

Fashion++: Minimal Edits for Outfit Improvement

International Conference on Computer Vision (ICCV)

Given an outfit, what small changes would most improve its fashionability? This question presents an intriguing new vision challenge. We introduce Fashion++, an approach that proposes minimal adjustments to a full-body clothing outfit that will have maximal impact on its fashionability.

By: Wei-Lin Hsiao, Isay Katsman, Chao-Yuan Wu, Devi Parikh, Kristen Grauman

October 20, 2019

Occlusions for Effective Data Augmentation in Image Classification

ICCV Workshop on Interpreting and Explaining Visual AI Models

In this paper, we show that, by using a simple technique based on batch augmentation, occlusions as data augmentation can result in better performance on ImageNet for high-capacity models (e.g., ResNet50). We also show that varying amounts of occlusions used during training can be used to study the robustness of different neural network architectures.

By: Ruth Fong, Andrea Vedaldi

October 20, 2019

Understanding Deep Networks via Extremal Perturbations and Smooth Masks

International Conference on Computer Vision (ICCV)

The problem of attribution is concerned with identifying the parts of an input that are responsible for a model’s output. An important family of attribution methods is based on measuring the effect of perturbations applied to the input. In this paper, we discuss some of the shortcomings of existing approaches to perturbation analysis and address them by introducing the concept of extremal perturbations, which are theoretically grounded and interpretable.

By: Ruth Fong, Mandela Patrick, Andrea Vedaldi

September 5, 2019

C3DPO: Canonical 3D Pose Networks for Non-Rigid Structure From Motion

International Conference on Computer Vision (ICCV)

We propose C3DPO, a method for extracting 3D models of deformable objects from 2D keypoint annotations in unconstrained images. We do so by learning a deep network that reconstructs a 3D object from a single view at a time, accounting for partial occlusions, and explicitly factoring the effects of viewpoint changes and object deformations.

By: David Novotny, Nikhila Ravi, Benjamin Graham, Natalia Neverova, Andrea Vedaldi

August 12, 2019

Efficient Segmentation: Learning Downsampling Near Semantic Boundaries

International Conference on Computer Vision (ICCV)

Many automated processes such as auto-piloting rely on a good semantic segmentation as a critical component. To speed up performance, it is common to downsample the input frame. However, this comes at the cost of missed small objects and reduced accuracy at semantic boundaries. To address this problem, we propose a new content-adaptive downsampling technique that learns to favor sampling locations near semantic boundaries of target classes.

By: Dmitrii Marin, Zijian He, Peter Vajda, Priyam Chatterjee, Sam Tsai, Fei Yang, Yuri Boykov

August 4, 2019

MSURU: Large Scale E-commerce Image Classification With Weakly Supervised Search Data

Conference on Knowledge Discovery and Data Mining (KDD)

In this paper we present a deployed image recognition system used in a large scale commerce search engine, which we call MSURU. It is designed to process product images uploaded daily to Facebook Marketplace. Social commerce is a growing area within Facebook and understanding visual representations of product content is important for search and recommendation applications on Marketplace.

By: Yina Tang, Fedor Borisyuk, Siddarth Malreddy, Yixuan Li, Yiqun Liu, Sergey Kirshner

July 31, 2019

Neural Volumes: Learning Dynamic Renderable Volumes from Images

SIGGRAPH

To overcome memory limitations of voxel-based representations, we learn a dynamic irregular grid structure implemented with a warp field during ray-marching. This structure greatly improves the apparent resolution and reduces grid-like artifacts and jagged motion. Finally, we demonstrate how to incorporate surface-based representations into our volumetric-learning framework for applications where the highest resolution is required, using facial performance capture as a case in point.

By: Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, Yaser Sheikh

July 14, 2019

Extreme Relative Pose Estimation for RGB-D Scans via Scene Completion

Conference on Computer Vision and Pattern Recognition (CVPR)

Estimating the relative rigid pose between two RGB-D scans of the same underlying environment is a fundamental problem in computer vision, robotics, and computer graphics. Most existing approaches allow only limited relative pose changes since they require considerable overlap between the input scans. We introduce a novel approach that extends the scope to extreme relative poses, with little or even no overlap between the input scans.

By: Zhenpei Yang, Jeffrey Z. Pan, Linjie Luo, Xiaowei Zhou, Kristen Grauman, Qixing Huang

July 12, 2019

VR Facial Animation via Multiview Image Translation

SIGGRAPH

In this work, we present a bidirectional system that can animate avatar heads of both users’ full likeness using consumer-friendly headset mounted cameras (HMC). There are two main challenges in doing this: unaccommodating camera views and the image-to-avatar domain gap. We address both challenges by leveraging constraints imposed by multiview geometry to establish precise image-to-avatar correspondence, which are then used to learn an end-to-end model for real-time tracking.

By: Shih-En Wei, Jason Saragih, Tomas Simon, Adam W. Harley, Stephen Lombardi, Michal Perdoch, Alexander Hypes, Dawei Wang, Hernan Badino, Yaser Sheikh