195 Results
July 9, 2018
Continuous Reasoning: Scaling the Impact of Formal Methods
Logic in Computer Science
This paper describes work in continuous reasoning, where formal reasoning about a (changing) codebase is done in a fashion which mirrors the iterative, continuous model of software development that is increasingly practiced in industry. We suggest that advances in continuous reasoning will allow formal reasoning to scale to more programs, and more programmers.
By: Peter O'Hearn
Systems & Networking June 19, 2018
Link and code: Fast indexing with graphs and compact regression codes
Computer Vision and Pattern Recognition (CVPR)
Similarity search approaches based on graph walks have recently attained outstanding speed-accuracy trade-offs, taking aside the memory requirements. In this paper, we revisit these approaches by considering, additionally, the memory constraint required to index billions of images on a single server.
By: Matthijs Douze, Alexandre Sablayrolles, Hervé Jégou
Facebook AI Research June 19, 2018
A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts
Computer Vision and Pattern Recognition (CVPR)
Most existing zero-shot learning methods consider the problem as a visual semantic embedding one. Given the demonstrated capability of Generative Adversarial Networks (GANs) to generate images, we instead leverage GANs to imagine unseen categories from text descriptions and hence recognize novel classes with no examples being seen.
By: Yizhe Zhu, Mohamed Elhoseiny, Bingchen Liu, Xi Peng, Ahmed Elgammal
Computer Vision 
Machine Learning June 19, 2018
LAMV: Learning to align and match videos with kernelized temporal layers
Computer Vision and Pattern Recognition (CVPR)
This paper considers a learnable approach for comparing and aligning videos. Our architecture builds upon and revisits temporal match kernels within neural networks: we propose a new temporal layer that finds temporal alignments by maximizing the scores between two sequences of vectors, according to a time-sensitive similarity metric parametrized in the Fourier domain.
By: Lorenzo Baraldi, Matthijs Douze, Rita Cucchiara, Hervé Jégou
Facebook AI Research June 18, 2018
Deep Spatio-Temporal Random Fields for Efficient Video Segmentation
Computer Vision and Pattern Recognition (CVPR)
In this work we introduce a time- and memory-efficient method for structured prediction that couples neuron decisions across both space at time. We show that we are able to perform exact and efficient inference on a densely connected spatio-temporal graph by capitalizing on recent advances on deep Gaussian random fields.
By: Siddhartha Chandra, Camille Couprie, Iasonas KokkinosJune 18, 2018
Separating Self-Expression and Visual Content in Hashtag Supervision
Computer Vision and Pattern Recognition (CVPR)
This paper presents an approach that extends upon modeling simple image-label pairs with a joint model of images, hashtags, and users. We demonstrate the efficacy of such approaches in image tagging and retrieval experiments, and show how the joint model can be used to perform user-conditional retrieval and tagging.
By: Andreas Veit, Maximilian Nickel, Serge Belongie, Laurens van der MaatenJune 18, 2018
Detect-and-Track: Efficient Pose Estimation in Videos
Computer Vision and Pattern Recognition (CVPR)
This paper addresses the problem of estimating and tracking human body keypoints in complex, multi-person video. We propose an extremely lightweight yet highly effective approach that builds upon the latest advancements in human detection [17] and video understanding [5].
By: Rohit Girdhar, Georgia Gkioxari, Lorenzo Torresani, Manohar Paluri, Du TranJune 18, 2018
Learning by Asking Questions
Computer Vision and Pattern Recognition (CVPR)
We introduce an interactive learning framework for the development and testing of intelligent visual systems, called learning-by-asking (LBA). We explore LBA in context of the Visual Question Answering (VQA) task.
By: Ishan Misra, Ross Girshick, Rob Fergus, Martial Hebert, Abhinav Gupta, Laurens van der MaatenJune 18, 2018
Non-Local Neural Networks
Computer Vision and Pattern Recognition (CVPR)
Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time. In this paper, we present non-local operations as a generic family of building blocks for capturing long-range dependencies.
By: Xiaolong Wang, Ross Girshick, Abhinav Gupta, Kaiming HeJune 18, 2018
3D Semantic Segmentation with Submanifold Sparse Convolutional Networks
Computer Vision and Pattern Recognition (CVPR)
We introduce new sparse convolutional operations that are designed to process spatially-sparse data more efficiently, and use them to develop spatially-sparse convolutional networks.
By: Benjamin Graham, Laurens van der Maaten, Martin Engelcke