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236 Results

October 22, 2017

Speaking the Same Language: Matching Machine to Human Captions by Adversarial Training

International Conference on Computer Vision (ICCV)

While strong progress has been made in image captioning recently, machine and human captions are still quite distinct. To address the challenges in this area, we change the training objective of the caption generator from reproducing ground-truth captions to generating a set of captions that is indistinguishable from human written captions.

By: Rakshith Shetty, Marcus Rohrbach, Lisa Anne Hendricks, Mario Fritz, Bernt Schiele
October 22, 2017

Predicting Deeper into the Future of Semantic Segmentation

International Conference on Computer Vision (ICCV)

The ability to predict and therefore to anticipate the future is an important attribute of intelligence. We introduce the novel task of predicting semantic segmentations of future frames. Given a sequence of video frames, our goal is to predict segmentation maps of not yet observed video frames that lie up to a second or further in the future.

By: Pauline Luc, Natalia Neverova, Camille Couprie, Jakob Verbeek, Yann LeCun
October 22, 2017

Transitive Invariance for Self-supervised Visual Representation Learning

International Conference on Computer Vision (ICCV)

In this paper, we propose to exploit different self-supervised approaches to learn representations invariant to (i) inter-instance variations (two objects in the same class should have similar features) and (ii) intra-instance variations (viewpoint, pose, deformations, illumination, etc.).

By: Xiaolong Wang*, Kaiming He, Abhinav Gupta
October 22, 2017

Focal Loss for Dense Object Detection

International Conference on Computer Vision (ICCV)

In this paper, we investigate why one-stage detectors that are applied over a regular, dense sampling of possible object locations have the potential to be faster and simpler, but have trailed the accuracy of two-stage detectors thus far. We design and train a simple dense detector we call RetinaNet. Our results show that when trained with the focal loss, RetinaNet is able to match the speed of previous one-stage detectors while surpassing the accuracy of all existing state-of-the-art two-stage detectors.

By: Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, Piotr Dollar
October 22, 2017

Mask R-CNN

International Conference on Computer Vision (ICCV)

We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition.

By: Kaiming He, Georgia Gkioxari, Piotr Dollar, Ross Girshick
October 22, 2017

Segmentation-Aware Convolutional Networks using Local Attention Masks

International Conference on Computer Vision (ICCV)

We introduce an approach to integrate segmentation information within a convolutional neural network (CNN). This counter-acts the tendency of CNNs to smooth information across regions and increases their spatial precision.

By: Adam W. Harley, Konstantinos G. Derpanis, Iasonas Kokkinos
October 22, 2017

Unsupervised Creation of Parameterized Avatars

International Conference on Computer Vision (ICCV)

We study the problem of mapping an input image to a tied pair consisting of a vector of parameters and an image that is created using a graphical engine from the vector of parameters. The mapping’s objective is to have the output image as similar as possible to the input image.

By: Lior Wolf, Yaniv Taigman, Adam Polyak
October 22, 2017

Learning to Reason: End-to-End Module Networks for Visual Question Answering

International Conference on Computer Vision (ICCV)

In this paper, we propose End-to-End Module Networks (N2NMNs), which learn to reason by directly predicting instance-specific network layouts without the aid of a parser.

By: Ronghang Hu, Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Kate Saenko
October 22, 2017

Inferring and Executing Programs for Visual Reasoning

International Conference on Computer Vision (ICCV)

Inspired by module networks, this paper proposes a model for visual reasoning that consists of a program generator that constructs an explicit representation of the reasoning process to be performed, and an execution engine that executes the resulting program to produce an answer.

By: Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Judy Hoffman, Li Fei-Fei, Larry Zitnick, Ross Girshick
October 22, 2017

Dense and Low-Rank Gaussian CRFs using Deep Embeddings

International Conference on Computer Vision (ICCV)

In this work we introduce a structured prediction model that endows the Deep Gaussian Conditional Random Field (G-CRF) with a densely connected graph structure.

By: Siddhartha Chandra, Nicolas Usunier, Iasonas Kokkinos