Papers by Abhishek Das

3 papers
Connecting Language and Vision to Actions (P18-5)

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Challenge: Recent advances in language and vision have made incredible progress in describing images and interacting with visual content in a physical or embodied environment.
Approach: This tutorial will provide an overview of the growing number of multimodal tasks and datasets that combine textual and visual understanding.
Outcome: This tutorial will review the state-of-the-art approaches to selected tasks such as image captioning, visual question answering and visual dialog.
Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering (D19-58)

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Challenge: Multi-hop question answering (QA) requires an information retrieval system that can find multiple supporting evidence needed to answer the question.
Approach: They propose a technique that uses information of entities present in the initial retrieved evidence to learn to ‘hop’ onto other relevant evidence.
Outcome: The proposed method boosts retrieval performance on a multi-hop question answering dataset with 5 million Wikipedia paragraphs and a model without training increases its performance by 10.59 F1.
Improving Generative Visual Dialog by Answering Diverse Questions (D19-1)

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Challenge: Prior work on training generative Visual Dialog models with reinforcement learning has shown that this improvement saturates and starts degrading after a few rounds of interaction, and does not lead to a better Visual Dialog model.
Approach: They propose a Q-Bot-A-Bot image-guessing game that allows Q-BOT to ask diverse questions, thus reducing repetitions and enabling A-BOTT to explore a larger state space during RL.
Outcome: The proposed approach improves Q-Bot-A-Bot image-guessing performance but degrades after a few rounds of interaction and does not lead to a better Visual Dialog model.

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