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550 Results

June 18, 2018

What Makes a Video a Video: Analyzing Temporal Information in Video Understanding Models and Datasets

Computer Vision and Pattern Recognition (CVPR)

While there have been numerous attempts at modeling motion in videos, an explicit analysis of the effect of temporal information for video understanding is still missing. In this work, we aim to bridge this gap and ask the following question: How important is the motion in the video for recognizing the action?

By: De-An Huang, Vignesh Ramanathan, Dhruv Mahajan, Lorenzo Torresani, Manohar Paluri, Li Fei-Fei, Juan Carlos Niebles
June 18, 2018

Improving Landmark Localization with Semi-Supervised Learning

Computer Vision and Pattern Recognition (CVPR)

We present two techniques to improve landmark localization in images from partially annotated datasets. Our primary goal is to leverage the common situation where precise landmark locations are only provided for a small data subset, but where class labels for classification or regression tasks related to the landmarks are more abundantly available.

By: Sina Honari, Pavlo Molchanov, Stephen Tyree, Pascal Vincent, Christopher Pal, Jan Kautz
June 18, 2018

Data Distillation: Towards Omni-Supervised Learning

Computer Vision and Pattern Recognition (CVPR)

We investigate omni-supervised learning, a special regime of semi-supervised learning in which the learner exploits all available labeled data plus internet-scale sources of unlabeled data.

By: Ilija Radosavovic, Piotr Dollar, Ross Girshick, Georgia Gkioxari, Kaiming He
June 18, 2018

Detail-Preserving Pooling in Deep Networks

Computer Vision and Pattern Recognition (CVPR)

In this paper, we aim to leverage recent results on image downscaling for the purposes of deep learning.

By: Faraz Saeedan, Nicolas Weber, Michael Goesele, Stefan Roth
June 18, 2018

Don’t Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering

Computer Vision and Pattern Recognition (CVPR)

A number of studies have found that today’s Visual Question Answering (VQA) models are heavily driven by superficial correlations in the training data and lack sufficient image grounding. To encourage development of models geared towards the latter, we propose a new setting for VQA where for every question type, train and test sets have different prior distributions of answers.

By: Aishwarya Agrawal, Dhruv Batra, Devi Parikh, Aniruddha Kembhavi
June 18, 2018

DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images

CVPR Workshop - DeepGlobe 2018

Similar to other challenges in computer vision domain such as DAVIS[21] and COCO[33], DeepGlobe proposes three datasets and corresponding evaluation methodologies, coherently bundled in three competitions with a dedicated workshop co-located with CVPR 2018.

By: Ilke Demir, Krzysztof Koperski, David Lindenbaum, Guan Pang, Jing Huang, Saikat Basu, Forest Hughes, Devis Tuia, Ramesh Raskar
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