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Research Area

Computer vision

Helping devices see and understand our visual world.

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  • Aayush Gupta, Ayush Jaiswal, Yue (Rex) Wu, Vivek Yadav, Pradeep Natarajan
    FG 2021
    2021
    We present a privacy preserving machine learning method for images that separates task-relevant information from task-irrelevant information. Our primary hypothesis is that by revealing the minimal number of pixels required for a task we can provide the most privacy preserving guarantees. Specifically, we propose an adversarial method that masks out task-irrelevant information from an image for preserving
  • NAACL 2021 Workshop on Visually Grounded Interaction and Language (ViGIL), ACL Findings 2022
    2021
    Interactive robots navigating photo-realistic environments need to be trained to effectively leverage and handle the dynamic nature of dialogue in addition to the challenges underlying vision-and-language navigation (VLN). In this paper, we present VISITRON, a multi-modal Transformer-based navigator better suited to the interactive regime inherent to Cooperative Vision-and-Dialog Navigation (CVDN). VISITRON
  • CVPR 2021 Fourth Workshop on Computer Vision for Fashion, Art and Design
    2021
    Fashion retrieval methods aim at learning a clothing-specific embedding space where images are ranked based on their global visual similarity with a given query. However, global embeddings struggle to capture localized fine-grained similarities between images, because of aggregation operations. Our work deals with this problem by learning localized representations for fashion retrieval based on local interest
  • Mohammed Suhail, Abhay Mittal, Behjat Siddiquie, Chris Broaddus, Jayan Eledath, Gérard Medioni, Leonid Sigal
    CVPR 2021
    2021
    Traditional scene graph generation methods are trained using cross-entropy losses that treat objects and relationships as independent entities. Such a formulation, however, ignores the structure in the output space, in an inherently structured prediction problem. In this work, we introduce a novel energy-based learning framework for generating scene graphs. The proposed formulation allows for efficiently
  • Tao Tu, Qing Ping, Govind Thattai, Gokhan Tur, Prem Natarajan
    CVPR 2021
    2021
    GuessWhat?! is a visual dialog guessing game which incorporates a Questioner agent that generates a sequence of questions, while an Oracle agent answers the respective questions about a target object in an image. Based on this dialog history between the Questioner and the Oracle, a Guesser agent makes a final guess of the target object. While previous work has focused on dialogue policy optimization and

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