• Publications
  • Influence
Multi-view Convolutional Neural Networks for 3D Shape Recognition
A longstanding question in computer vision concerns the representation of 3D shapes for recognition: should 3D shapes be represented with descriptors operating on their native 3D formats, such asExpand
  • 1,460
  • 246
  • PDF
FDDB: A benchmark for face detection in unconstrained settings
TLDR
We present a new data set of face images with more faces and more accurate annotations for face regions than in previous data sets. Expand
  • 748
  • 201
  • PDF
Distribution fields for tracking
TLDR
We introduce a method for building an image descriptor using distribution fields (DFs), a representation that allows smoothing the objective function without destroying information about pixel values. Expand
  • 532
  • 78
  • PDF
Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation
TLDR
We propose an end-to-end convolutional neural network for variable-length multi-frame video interpolation, where the motion interpretation and occlusion reasoning are jointly modeled. Expand
  • 219
  • 67
  • PDF
Labeled Faces in the Wild: A Survey
TLDR
In 2007, Labeled Faces in the Wild was released in an effort to spur research in face recognition, specifically for the problem of face verification with unconstrained images. Expand
  • 331
  • 61
  • PDF
Labeled Faces in the Wild : Updates and New Reporting Procedures
TLDR
The Labeled Faces in the Wild (LFW) database has spurred significant research in the problem of unconstrained face verification and other related problems. Expand
  • 242
  • 53
  • PDF
Face Detection with the Faster R-CNN
TLDR
We investigate applying the Faster RCNN, which has recently demonstrated impressive results on various object detection benchmarks, to face detection. Expand
  • 344
  • 42
  • PDF
Data driven image models through continuous joint alignment
  • E. Learned-Miller
  • Medicine, Computer Science
  • IEEE Transactions on Pattern Analysis and Machine…
  • 1 February 2006
TLDR
This paper presents a family of techniques that we call congealing for modeling image classes from data. Expand
  • 301
  • 34
  • PDF
Unsupervised Joint Alignment of Complex Images
TLDR
We describe a novel method to achieve this positioning using poorly aligned examples of a class with no additional labeling of parts or poses in the data. Expand
  • 322
  • 33
  • PDF
Augmenting CRFs with Boltzmann Machine Shape Priors for Image Labeling
TLDR
We propose the GLOC (GLObal and LOCal) model, a strong model for image labeling problems that combines the best properties of the CRF (that enforces local consistency between adjacent nodes) and the RBM (that models global shape prior of the 1 object). Expand
  • 153
  • 32
  • PDF