Papers

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Viewing 811-820 of 978 papers
  • ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation

    Sachin Mehta, Mohammad Rastegari, Anat Caspi, Linda Shapiro, and Hannaneh HajishirziECCV2018 We introduce a fast and efficient convolutional neural network, ESPNet, for semantic segmentation of high resolution images under resource constraints. ESPNet is based on a new convolutional module, efficient spatial pyramid (ESP), which is efficient in terms…
  • Event2Mind: Commonsense Inference on Events, Intents, and Reactions

    Maarten Sap, Hannah Rashkin, Emily Allaway, Noah A. Smith and Yejin ChoiACL2018 We investigate a new commonsense inference task: given an event described in a short free-form text (“X drinks coffee in the morning”), a system reasons about the likely intents (“X wants to stay awake”) and reactions (“X feels alert”) of the event’s…
  • Extending a Parser to Distant Domains Using a Few Dozen Partially Annotated Examples

    Vidur Joshi, Matthew Peters, and Mark HopkinsACL2018 We revisit domain adaptation for parsers in the neural era. First we show that recent advances in word representations greatly diminish the need for domain adaptation when the target domain is syntactically similar to the source domain. As evidence, we train…
  • Imagine This! Scripts to Compositions to Videos

    Tanmay Gupta, Dustin Schwenk, Ali Farhadi, Derek Hoiem, and Aniruddha KembhaviECCV2018 Imagining a scene described in natural language with realistic layout and appearance of entities is the ultimate test of spatial, visual, and semantic world knowledge. Towards this goal, we present the Composition, Retrieval and Fusion Network (Craft), a…
  • IQA: Visual Question Answering in Interactive Environments

    Daniel Gordon, Aniruddha Kembhavi, Mohammad Rastegari, Joseph Redmon, Dieter Fox, Ali FarhadiCVPR2018 We introduce Interactive Question Answering (IQA), the task of answering questions that require an autonomous agent to interact with a dynamic visual environment. IQA presents the agent with a scene and a question, like: “Are there any apples in the fridge…
  • Learning to Write with Cooperative Discriminators

    Ari Holtzman, Jan Buys, Maxwell Forbes, Antoine Bosselut, David Golub and Yejin ChoiACL2018 Despite their local fluency, long-form text generated from RNNs is often generic, repetitive, and even self-contradictory. We propose a unified learning framework that collectively addresses all the above issues by composing a committee of discriminators that…
  • LSTMs Exploit Linguistic Attributes of Data

    Nelson F. Liu, Omer Levy, Roy Schwartz, Chenhao Tan, Noah A. SmithACL • RepL4NLP Workshop2018 While recurrent neural networks have found success in a variety of natural language processing applications, they are general models of sequential data. We investigate how the properties of natural language data affect an LSTM's ability to learn a…
  • Modeling Naive Psychology of Characters in Simple Commonsense Stories

    Hannah Rashkin, Antoine Bosselut, Maarten Sap, Kevin Knight and Yejin ChoiACL2018 Understanding a narrative requires reading between the lines and reasoning about the unspoken but obvious implications about events and people’s mental states — a capability that is trivial for humans but remarkably hard for machines. To facilitate research…
  • Simple and Effective Multi-Paragraph Reading Comprehension

    Christopher Clark, Matt GardnerACL2018 We consider the problem of adapting neural paragraph-level question answering models to the case where entire documents are given as input. Our proposed solution trains models to produce well calibrated confidence scores for their results on individual…
  • Transferring Common-Sense Knowledge for Object Detection

    Krishna Kumar Singh, Santosh Kumar Divvala, Ali Farhadi, and Yong Jae LeeECCV2018 We propose the idea of transferring common-sense knowledge from source categories to target categories for scalable object detection. In our setting, the training data for the source categories have bounding box annotations, while those for the target…