Search Results for author:
Found 19 papers, 3 papers with code
Autoregressive Perturbations for Data Poisoning
Unfortunately, existing methods require knowledge of both the target architecture and the complete dataset so that a surrogate network can be trained, the parameters of which are used to generate the attack.
Fast Light-Weight Near-Field Photometric Stereo
Existing approaches rely on optimization coupled with a far-field PS network operating on pixels or small patches.
Shape and Material Capture at Home
In this paper, we present a technique for estimating the geometry and reflectance of objects using only a camera, flashlight, and optionally a tripod.
Neural Inverse Rendering of an Indoor Scene from a Single Image
Inverse rendering aims to estimate physical attributes of a scene, e. g., reflectance, geometry, and lighting, from image(s).
Label Denoising Adversarial Network (LDAN) for Inverse Lighting of Faces
We propose to train a deep Convolutional Neural Network (CNN) to regress lighting parameters from a single face image.
SfSNet: Learning Shape, Reflectance and Illuminance of Faces `in the Wild'
SfSNet learns from a mixture of labeled synthetic and unlabeled real world images.
End-to-end Recovery of Human Shape and Pose
The main objective is to minimize the reprojection loss of keypoints, which allow our model to be trained using images in-the-wild that only have ground truth 2D annotations.
Ranked #1 on
Weakly-supervised 3D Human Pose Estimation
on Human3.6M
(3D Annotations metric)
Label Denoising Adversarial Network (LDAN) for Inverse Lighting of Face Images
We propose to train a deep Convolutional Neural Network (CNN) to regress lighting parameters from a single face image.
Seeing What Is Not There: Learning Context to Determine Where Objects Are Missing
Combined with object detection results, we can perform a novel vision task: finding where objects are missing in an image.
A New Rank Constraint on Multi-view Fundamental Matrices, and its Application to Camera Location Recovery
We show that in general, with the selection of proper scale factors, a matrix formed by stacking fundamental matrices between pairs of images has rank 6.
Solving Uncalibrated Photometric Stereo Using Fewer Images by Jointly Optimizing Low-rank Matrix Completion and Integrability
We introduce a new, integrated approach to uncalibrated photometric stereo.
WarpNet: Weakly Supervised Matching for Single-view Reconstruction
This is in contrast to prior works that require part annotations, since matching objects across class and pose variations is challenging with appearance features alone.
Learning 3D Deformation of Animals from 2D Images
In this paper, we show that such information can be learned from user-clicked 2D images and a template 3D model of the target animal.
Deep Hierarchical Parsing for Semantic Segmentation
We propose to tackle this problem by including the classification loss of the internal nodes of the random parse trees in the original RCPN loss function.
Riemannian Metric Learning for Symmetric Positive Definite Matrices
In this work, we focus on the log-Euclidean Riemannian geometry and propose a data-driven approach for learning Riemannian metrics/geodesic distances for SPD matrices.
Birdsnap: Large-scale Fine-grained Visual Categorization of Birds
We show how these priors can be used to significantly improve performance.
From Shading to Local Shape
We develop a framework for extracting a concise representation of the shape information available from diffuse shading in a small image patch.
Sparse Norm Filtering
Optimization-based filtering smoothes an image by minimizing a fidelity function and simultaneously preserves edges by exploiting a sparse norm penalty over gradients.
Spectral Graph Cut from a Filtering Point of View
In this paper, we analyze image segmentation algorithms that are based on spectral graph theory, e. g., normalized cut, and show that there is a natural connection between spectural graph theory based image segmentationand and edge preserving filtering.
