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Year Published

520 Results

June 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 Maaten
June 18, 2018

Canonical Tensor Decomposition for Knowledge Base Completion

International Conference on Machine Learning (ICML)

The problem of Knowledge Base Completion can be framed as a 3rd-order binary tensor completion problem. In this light, the Canonical Tensor Decomposition (CP) (Hitchcock, 1927) seems like a natural solution. However, current implementations of CP on standard Knowledge Base Completion benchmarks are lagging behind their competitors. In this work, we attempt to understand the limits of CP for knowledge base completion.

By: Timothée Lacroix, Nicolas Usunier, Guillaume Obozinski
June 18, 2018

Low-Shot Learning from Imaginary Data

Computer Vision and Pattern Recognition (CVPR)

Humans can quickly learn new visual concepts, perhaps because they can easily visualize or imagine what novel objects look like from different views. Incorporating this ability to hallucinate novel instances of new concepts might help machine vision systems perform better low-shot learning, i.e., learning concepts from few examples. We present a novel approach to low-shot learning that uses this idea.

By: Yu-Xiong Wang, Ross Girshick, Martial Hebert, Bharath Hariharan
June 18, 2018

Low-shot learning with large-scale diffusion

Computer Vision and Pattern Recognition (CVPR)

This paper considers the problem of inferring image labels from images when only a few annotated examples are available at training time.

By: Matthijs Douze, Arthur Szlam, Bharath Hariharan, Hervé Jégou
June 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 Maaten
June 18, 2018

Learning to Segment Every Thing

Computer Vision and Pattern Recognition (CVPR)

The goal of this paper is to propose a new partially supervised training paradigm, together with a novel weight transfer function, that enables training instance segmentation models on a large set of categories all of which have box annotations, but only a small fraction of which have mask annotations.

By: Ronghang Hu, Piotr Dollar, Kaiming He, Trevor Darrell, Ross Girshick
June 18, 2018

Stacked Latent Attention for Multimodal Reasoning

Computer Vision and Pattern Recognition (CVPR)

Attention has shown to be a pivotal development in deep learning and has been used for a multitude of multimodal learning tasks such as visual question answering and image captioning. In this work, we pinpoint the potential limitations to the design of a traditional attention model.

By: Haoqi Fan, Jiatong Zhou
June 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
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 Kokkinos
June 18, 2018

Modeling Facial Geometry using Compositional VAEs

Computer Vision and Pattern Recognition (CVPR)

We propose a method for learning non-linear face geometry representations using deep generative models. Our model is a variational autoencoder with multiple levels of hidden variables where lower layers capture global geometry and higher ones encode more local deformations.

By: Timur Bagautdinov, Chenglei Wu, Jason Saragih, Pascal Fua, Yaser Sheikh