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220 Results

June 18, 2018

On the iterative refinement of densely connected representation levels for semantic segmentation

CVPR Workshop (CVPRW) on Autonomous Driving

In this paper, we systematically study the differences introduced by distinct receptive field enlargement methods and their impact on the performance of a novel architecture, called Fully Convolutional DenseResNet (FC-DRN).

By: Arantxa Casanova, Guillem Cucurull, Michal Drozdzal, Adriana Romero, Yoshua Bengio
June 18, 2018

Multimodal Explanations: Justifying Decisions and Pointing to the Evidence

Computer Vision and Pattern Recognition (CVPR)

Deep models that are both effective and explainable are desirable in many settings; prior explainable models have been unimodal, offering either image-based visualization of attention weights or text-based generation of post-hoc justifications. We propose a multimodal approach to explanation, and argue that the two modalities provide complementary explanatory strengths.

By: Dong Huk Park, Lisa Anne Hendricks, Zeynep Akata, Anna Rohrbach, Bernt Schiele, Trevor Darrell, Marcus Rohrbach
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

Audio to Body Dynamics

Computer Vision and Pattern Recognition (CVPR)

We present a method that gets as input an audio of violin or piano playing, and outputs a video of skeleton predictions which are further used to animate an avatar. The key idea is to create an animation of an avatar that moves their hands similarly to how a pianist or violinist would do, just from audio.

By: Eli Shlizerman, Lucio Dery, Hayden Schoen, Ira Kemelmacher Shlizerman
June 18, 2018

DeepMVS: Learning Multi-view Stereopsis

Computer Vision and Pattern Recognition (CVPR)

We present DeepMVS, a deep convolutional neural network (ConvNet) for multi-view stereo reconstruction.

By: Po-Han Huang, Kevin Matzen, Johannes Kopf, Narendra Ahuja, Jia-Bin Huang
June 18, 2018

Detecting and Recognizing Human-Object Interactions

Computer Vision and Pattern Recognition (CVPR)

In this paper, we address the task of detecting triplets in challenging everyday photos. We propose a novel model that is driven by a human-centric approach. Our hypothesis is that the appearance of a person – their pose, clothing, action – is a powerful cue for localizing the objects they are interacting with.

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

DensePose: Dense Human Pose Estimation In The Wild

Computer Vision and Pattern Recognition (CVPR)

In this work we establish dense correspondences between an RGB image and a surface-based representation of the human body, a task we refer to as dense human pose estimation. We gather dense correspondences for 50K persons appearing in the COCO dataset by introducing an efficient annotation pipeline. We then use our dataset to train CNN-based systems that deliver dense correspondence ‘in the wild’, namely in the presence of background, occlusions and scale variations.

By: Riza Alp Guler, Natalia Neverova, Iasonas Kokkinos
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

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

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