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52 Results

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
May 2, 2018

Exploring the Limits of Weakly Supervised Pretraining


In this paper, we present a unique study of transfer learning with large convolutional networks trained to predict hashtags on billions of social media images.

By: Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, Laurens van der Maaten
April 30, 2018

Multi-Scale Dense Networks for Resource Efficient Image Classification

International Conference on Learning Representations (ICLR)

In this paper we investigate image classification with computational resource limits at test time. Two such settings are: 1. anytime classification, where the network’s prediction for a test example is progressively updated, facilitating the output of a prediction at any time; and 2. budgeted batch classification, where a fixed amount of computation is available to classify a set of examples that can be spent unevenly across “easier” and “harder” inputs.

By: Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens van der Maaten, Kilian Weinberger
April 30, 2018

Identifying Analogies Across Domains

International Conference on Learning Representations (ICLR)

In this paper, we tackle this very task of finding exact analogies between datasets i.e. for every image from domain A find an analogous image in domain B. We present a matching-by-synthesis approach: AN-GAN, and show that it outperforms current techniques.

By: Yedid Hoshen, Lior Wolf
April 30, 2018

Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks

International Conference on Learning Representations (ICLR)

We consider the problem of detecting out-of-distribution images in neural networks. We propose ODIN, a simple and effective method that does…

By: Shiyu Liang, Yixuan Li, R. Srikant
December 4, 2017

One-Sided Unsupervised Domain Mapping

Neural Information Processing Systems (NIPS)

In this work, we present a method of learning GAB without learning GBA. This is done by learning a mapping that maintains the distance between a pair of samples.

By: Sagie Benaim, Lior Wolf
November 27, 2017

Casual 3D Photography


We present an algorithm that enables casual 3D photography. Given a set of input photos captured with a hand-held cell phone or DSLR camera, our algorithm reconstructs a 3D photo, a central panoramic, textured, normal mapped, multi-layered geometric mesh representation. Our geometric representation also allows interacting with the scene using 3D geometry-aware effects, such as adding new objects to the scene and artistic lighting effects.

By: Peter Hedman, Suhib Alsisan, Richard Szeliski, Johannes Kopf
November 27, 2017

Bringing Portraits to Life


We present a technique to automatically animate a still portrait, making it possible for the subject in the photo to come to life and express various emotions. We use a driving video (of a different subject) and develop means to transfer the expressiveness of the subject in the driving video to the target portrait. In contrast to previous work that requires an input video of the target face to reenact a facial performance, our technique uses only a single target image.

By: Hadar Averbuch-Elor, Daniel Cohen-Or, Johannes Kopf, Michael Cohen
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
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