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557 Results

September 7, 2018

Object Level Visual Reasoning in Videos

European Conference on Computer Vision (ECCV)

We propose a model capable of learning to reason about semantically meaningful spatio-temporal interactions in videos.

By: Fabien Baradel, Natalia Neverova, Christian Wolf, Julien Mille, Greg Mori
September 7, 2018

Group Normalization

European Conference on Computer Vision (ECCV)

Batch Normalization (BN) is a milestone technique in the development of deep learning, enabling various networks to train. However, normalizing along the batch dimension introduces problems — BN’s error increases rapidly when the batch size becomes smaller, caused by inaccurate batch statistics estimation. This limits BN’s usage for training larger models and transferring features to computer vision tasks including detection, segmentation, and video, which require small batches constrained by memory consumption. In this paper, we present Group Normalization (GN) as a simple alternative to BN.

By: Yuxin Wu, Kaiming He
September 7, 2018

Joint Future Semantic and Instance Segmentation Prediction

ECCV Anticipating Human Behavior Workshop

In this work, we introduce a novel prediction approach that encodes instance and semantic segmentation information in a single representation based on distance maps.

By: Camille Couprie, Pauline Luc, Jakob Verbeek
September 7, 2018

Recycle-GAN: Unsupervised Video Retargeting

European Conference on Computer Vision (ECCV)

We introduce a data-driven approach for unsupervised video retargeting that translates content from one domain to another while preserving the style native to a domain, i.e., if contents of John Oliver’s speech were to be transferred to Stephen Colbert, then the generated content/speech should be in Stephen Colbert’s style.

By: Aayush Bansal, Shugao Ma, Deva Ramanan, Yaser Sheikh
September 3, 2018

ConvNets and ImageNet Beyond Accuracy: Understanding Mistakes and Uncovering Biases

European Conference on Computer Vision (ECCV)

ConvNets and ImageNet have driven the recent success of deep learning for image classification. However, the marked slowdown in performance improvement combined with the lack of robustness of neural networks to adversarial examples and their tendency to exhibit undesirable biases question the reliability of these methods. This work investigates these questions from the perspective of the end-user by using human subject studies and explanations.

By: Pierre Stock, Moustapha Cisse
August 27, 2018

Providing Streaming Joins as a Service at Facebook

International Conference on Very Large Data Bases (VLDB)

This paper describes an end-to-end streaming join service that addresses the challenges above through a streaming join operator that uses an adaptive stream synchronization algorithm that is able to handle the different distributions we observe in real-world streams regarding their event times.

By: Gabriela Jacques da Silva, Ran Lei, Luwei Cheng, Guoqiang Jerry Chen, Kuen Ching, Tanji Hu, Yuan Mei, Kevin Wilfong, Rithin Shetty, Serhat Yilmaz, Anirban Banerjee, Benjamin Heintz, Shridhar Iyer, Anshul Jaiswal
August 26, 2018

Rosetta: Large Scale System for Text Detection and Recognition in Images

Knowledge Discovery in Databases (KDD)

In this paper we present a deployed, scalable optical character recognition (OCR) system, which we call Rosetta, designed to process images uploaded daily at Facebook scale.

By: Fedor Borisyuk, Albert Gordo, Viswanath Sivakumar
August 22, 2018

FBOSS: Building Switch Software at Scale


We present FBOSS, our own data center switch software, that is designed with the basis on our switch-as-a-server and deploy-early-and-iterate principles.

By: Sean Choi, Boris Burkov, Alex Eckert, Tian Fang, Saman Kazemkhani, Rob Sherwood, Ying Zhang, James Hongyi Zeng
August 20, 2018

TaxoGen: Unsupervised Topic Taxonomy Construction by Adaptive Term Embedding and Clustering

Knowledge Discovery in Databases (KDD)

In this paper, we propose a method for constructing topic taxonomies, wherein every node represents a conceptual topic and is defined as a cluster of semantically coherent concept terms.

By: Chao Zhang, Fangbo Tao, Xiusi Chen, Jiaming Shen, Meng Jiang, Brian Sadler, Michelle Vanni, Jiawei Han
August 19, 2018

A real-time framework for detecting efficiency regressions in a globally distributed codebase

Knowledge Discovery in Databases (KDD)

This paper describes the end-to-end regression detection system designed and used at Facebook. The main detection algorithm is based on sequential statistics supplemented by signal processing transformations, and the performance of the algorithm was assessed with a mixture of online and offline tests across different use cases.

By: Martin Valdez-Vivas, Caner Gocmen, Andrii Korotkov, Ethan Fang, Kapil Goenka, Sherry Chen