May 16, 2017

Cultural Diffusion and Trends in Facebook Photographs

The International AAAI Conference on Web and Social Media (ICWSM)

Online social media is a social vehicle in which people share various moments of their lives with their friends, such as playing sports, cooking dinner or just taking a selfie for fun, via visual means, i.e., photographs. Our study takes a closer look at the popular visual concepts illustrating various cultural lifestyles from aggregated, de-identified photographs.

Quenzeng You, Dario Garcia, Manohar Paluri, Jiebo Luo, Jungseock Joo
February 22, 2017

Automatic Alt-text: Computer-generated Image Descriptions for Blind Users on a Social Network Service


This paper covers the design and deployment of an automatic alt-text (AAT), a system that applies computer vision technology to identify faces, objects, and themes from photos to generate photo alt-text for screen reader users on Facebook.

Jeffrey Wieland, Julie Schiller, Omid Farivar, Shaomei Wu
December 6, 2016

Feedback Neural Network for Weakly Supervised Geo-Semantic Segmentation


We propose a novel neural network architecture to perform weakly-supervised learning by suppressing irrelevant neuron activations. When applied to a practical challenge of transforming satellite images into a map of settlements and individual buildings it delivers results that show superior performance and efficiency.

Xianming Liu, Amy Zhang, Tobias Tiecke, Andreas Gros, Thomas S. Huang
December 6, 2016

Population Density Estimation with Deconvolutional Neural Networks

Workshop on Large Scale Computer Vision at NIPS 2016

This work is part of the initiative to provide connectivity all over the world. Population density data is helpful in driving a variety of technology decisions, but currently, a microscopic dataset of population doesn’t exist. Current state of the art population density datasets are at ~1000km2 resolution. To create a better dataset, we have obtained 1PB of satellite imagery at 50cm/pixel resolution to feed through our building classification pipeline.

Amy Zhang, Andreas Gros, Tobias Tiecke, Xianming Liu
October 10, 2016

Learning to Refine Object Segments

European Conference on Computer Vision

In this work we propose to augment feedforward nets for object segmentation with a novel top-down refinement approach.

Pedro O. Pinheiro, Tsung-Yi Lin, Ronan Collobert, Piotr Dollar
September 18, 2016

A MultiPath Network for Object Detection


We test three modifications to the standard Fast R-CNN object detector to determine if they can overcome the object detection challenges in a COCO object detection dataset.

Sergey Zagoruyko, Adam Lerer, Tsung-Yi Lin, Pedro O. Pinheiro, Sam Gross, Soumith Chintala, Piotr Dollar
July 25, 2016

Single Image 3D Interpreter Network

European Conference on Computer Vision (ECCV)

In this work, we propose 3D INterpreter Network (3D-INN), an end-to-end framework which sequentially estimates 2D keypoint heatmaps and 3D object structure, trained on both real 2D-annotated images and synthetic 3D data.

Antonio Torralba, Jiajun Wu, Joseph J. Lim, Joshua B. Tenenbaum, Tianfan Xue, William T. Freeman, Yuandong Tian
June 27, 2016

Unsupervised Learning of Edges


Data-driven approaches for edge detection have proven effective and achieve top results on modern benchmarks. However, all current data-driven edge detectors require manual supervision for training in the form of hand-labeled region segments or object boundaries.

Yin Li, Manohar Paluri, James M. Rehg, Piotr Dollar
June 18, 2016

Learning Physical Intuition of Block Towers by Example

International Conference on Machine Learning

Wooden blocks are a common toy for infants, allowing them to develop motor skills and gain intuition about the physical behavior of the world. In this paper, we explore the ability of deep feed-forward models to learn such intuitive physics.

Adam Lerer, Sam Gross, Rob Fergus
May 2, 2016

Metric Learning with Adaptive Density Discrimination


Distance metric learning approaches learn a transformation to a representation space in which distance is in correspondence with a predefined notion of similarity.

Oren Rippel, Manohar Paluri, Piotr Dollar, Lubomir Bourdev
December 15, 2015

Learning to Segment Object Candidates


In this paper, we propose a new way to generate object proposals, introducing an approach based on a discriminative convolutional network. Our model obtains substantially higher object recall using fewer proposals. We also show that our model is able to generalize to unseen categories it has not seen during training.

Pedro Oliveira, Ronan Collobert, Piotr Dollar
February 17, 2015

What Makes for Effective Detection Proposals?


An in depth study of object proposals and their effect on object detection performance.

Bernt Schiele, Jan Hosang, Piotr Dollar, Rodrigo Benenson