174 citations · 711 across the 57 of their papers we have counts for
7 papers · 1 filter
Learning Non-Lambertian Object Intrinsics across ShapeNet Categories
Jian Shi, Yue Dong, Hao Su +1
We consider the non-Lambertian object intrinsic problem of recovering diffuse albedo, shading, and specular highlights from a single image of an object. We build a large-scale obje…
Multigrid Neural Architectures
Tsung-Wei Ke, Michael Maire, Stella X. Yu
We propose a multigrid extension of convolutional neural networks (CNNs). Rather than manipulating representations living on a single spatial grid, our network layers operate acros…
Am I a Baller? Basketball Performance Assessment from First-Person Videos
Gedas Bertasius, Hyun Soo Park, Stella X. Yu +1
This paper presents a method to assess a basketball player's performance from his/her first-person video. A key challenge lies in the fact that the evaluation metric is highly subj…
Unsupervised Learning of Important Objects from First-Person Videos
Gedas Bertasius, Hyun Soo Park, Stella X. Yu +1
A first-person camera, placed at a person's head, captures, which objects are important to the camera wearer. Most prior methods for this task learn to detect such important object…
Fine-to-coarse Knowledge Transfer For Low-Res Image Classification
Xingchao Peng, Judy Hoffman, Stella X. Yu +1
We address the difficult problem of distinguishing fine-grained object categories in low resolution images. Wepropose a simple an effective deep learning approach that transfers fi…
Convolutional Random Walk Networks for Semantic Image Segmentation
Gedas Bertasius, Lorenzo Torresani, Stella X. Yu +1
Most current semantic segmentation methods rely on fully convolutional networks (FCNs). However, their use of large receptive fields and many pooling layers cause low spatial resol…
First Person Action-Object Detection with EgoNet
Gedas Bertasius, Hyun Soo Park, Stella X. Yu +1
Unlike traditional third-person cameras mounted on robots, a first-person camera, captures a person's visual sensorimotor object interactions from up close. In this paper, we study…