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Year Published

127 Results

December 4, 2017

Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model

Neural Information Processing Systems (NIPS)

We present a novel training framework for neural sequence models, particularly for grounded dialog generation.

By: Jiasen Lu, Anitha Kannan, Jianwei Yang, Devi Parikh, Dhruv Batra
December 4, 2017

Fader Networks: Manipulating Images by Sliding Attributes

Neural Information Processing Systems (NIPS)

This paper introduces a new encoder-decoder architecture that is trained to reconstruct images by disentangling the salient information of the image and the values of attributes directly in the latent space.

By: Guillaume Lample, Neil Zeghidour, Nicolas Usunier, Antoine Bordes, Ludovic Denoyer, Marc'Aurelio Ranzato
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
December 4, 2017

ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games

Neural Information Processing Systems (NIPS)

In this paper, we propose ELF, an Extensive, Lightweight and Flexible platform for fundamental reinforcement learning research.

By: Yuandong Tian, Qucheng Gong, Wenling Shang, Yuxin Wu, Larry Zitnick
October 24, 2017

Evaluating Visual Conversational Agents via Cooperative Human-AI Games

AAAI Conference on Human Computation and Crowdsourcing (HCOMP)

In this work, we design a cooperative game – GuessWhich – to measure human-AI team performance in the specific context of the AI being a visual conversational agent.

By: Prithvijit Chattopadhyay, Deshraj Yadav, Viraj Prabhu, Arjun Chandrasekaran, Abhishek Das, Stefan Lee, Dhruv Batra, Devi Parikh
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

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

International Conference on Computer Vision (ICCV)

We propose a technique for producing ‘visual explanations’ for decisions from a large class of Convolutional Neural Network (CNN)-based models, making them more transparent and explainable.

By: Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, Dhruv Batra
October 22, 2017

Learning Visual N-Grams from Web Data

International Conference on Computer Vision (ICCV)

This paper explores the training of image-recognition systems on large numbers of images and associated user comments, without using manually labeled images.

By: Ang Li, Allan Jabri, Armand Joulin, Laurens van der Maaten
October 22, 2017

Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning

International Conference on Computer Vision (ICCV)

We introduce the first goal-driven training for visual question answering and dialog agents. Specifically, we pose a cooperative ‘image guessing’ game between two agents – Q-BOT and A-BOT– who communicate in natural language dialog so that Q-BOT can select an unseen image from a lineup of images.

By: Abhishek Das, Satwik Kottur, José M.F. Moura, Stefan Lee, Dhruv Batra
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