Papers

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Viewing 431-440 of 571 papers
  • Neural Motifs: Scene Graph Parsing with Global Context

    Rowan Zellers, Mark Yatskar, Sam Thomson, Yejin ChoiCVPR2018 We investigate the problem of producing structured graph representations of visual scenes. Our work analyzes the role of motifs: regularly appearing substructures in scene graphs. We present new quantitative insights on such repeated structures in the Visual…
  • Neural Poetry Translation

    Marjan Ghazvininejad, Yejin Choi and Kevin KnightNAACL2018 We present the first neural poetry translation system. Unlike previous works that often fail to produce any translation for fixed rhyme and rhythm patterns, our system always translates a source text to an English poem. Human evaluation ranks translation…
  • SeGAN: Segmenting and Generating the Invisible

    Kiana Ehsani, Roozbeh Mottaghi, Ali FarhadiCVPR2018 Objects often occlude each other in scenes; Inferring their appearance beyond their visible parts plays an important role in scene understanding, depth estimation, object interaction and manipulation. In this paper, we study the challenging problem of…
  • SoPa: Bridging CNNs, RNNs, and Weighted Finite-State Machines

    Roy Schwartz, Sam Thomson and Noah A. SmithACL2018 Recurrent and convolutional neural networks comprise two distinct families of models that have proven to be useful for encoding natural language utterances. In this paper we present SoPa, a new model that aims to bridge these two approaches. SoPa combines…
  • Structured Set Matching Networks for One-Shot Part Labeling

    Jonghyun Choi, Jayant Krishnamurthy, Aniruddha Kembhavi, Ali FarhadiCVPR2018 Diagrams often depict complex phenomena and serve as a good test bed for visual and textual reasoning. However, understanding diagrams using natural image understanding approaches requires large training datasets of diagrams, which are very hard to obtain…
  • Tracking State Changes in Procedural Text: A Challenge Dataset and Models for Process Paragraph Comprehension

    Bhavana Dalvi, Lifu Huang, Niket Tandon, Wen-tau Yih, Peter ClarkNAACL2018 We present a new dataset and models for comprehending paragraphs about processes (e.g., photosynthesis), an important genre of text describing a dynamic world. The new dataset, ProPara, is the first to contain natural (rather than machine-generated) text…
  • VISIR: Visual and Semantic Image Label Refinement

    Sreyasi Nag Chowdhury, Niket Tandon, Hakan Ferhatosmanoglu, Gerhard WeikumWSDM2018 The social media explosion has populated the Internet with a wealth of images. There are two existing paradigms for image retrieval: 1)content-based image retrieval (BIR), which has traditionally used visual features for similarity search (e.g., SIFT features…
  • What Happened? Leveraging VerbNet to Predict the Effects of Actions in Procedural Text

    Peter Clark, Bhavana Dalvi, Niket TandonarXiv2018 Our goal is to answer questions about paragraphs describing processes (e.g., photosynthesis). Texts of this genre are challenging because the effects of actions are often implicit (unstated), requiring background knowledge and inference to reason about the…
  • Who Let The Dogs Out? Modeling Dog Behavior From Visual Data

    Kiana Ehsani, Hessam Bagherinezhad, Joe Redmon, Roozbeh Mottaghi, Ali FarhadiCVPR2018 We study the task of directly modelling a visually intelligent agent. Computer vision typically focuses on solving various subtasks related to visual intelligence. We depart from this standard approach to computer vision; instead we directly model a visually…
  • Ontology Alignment in the Biomedical Domain Using Entity Definitions and Context

    Lucy L. Wang, Chandra Bhagavatula, M. Neumann, Kyle Lo, Chris Wilhelm, Waleed AmmarACL • Proceedings of the BioNLP 2018 Workshop2018 Ontology alignment is the task of identifying semantically equivalent entities from two given ontologies. Different ontologies have different representations of the same entity, resulting in a need to de-duplicate entities when merging ontologies. We propose…