Search Results for author: Aishwarya Agrawal

Found 13 papers, 4 papers with code

Vision-Language Pretraining: Current Trends and the Future

no code implementations ACL 2022 Aishwarya Agrawal, Damien Teney, Aida Nematzadeh

In addition to the larger pretraining datasets, the transformer architecture (Vaswani et al., 2017) and in particular self-attention applied to two modalities are responsible for the impressive performance of the recent pretrained models on downstream tasks (Hendricks et al., 2021).

Question Answering Representation Learning +1

Rethinking Evaluation Practices in Visual Question Answering: A Case Study on Out-of-Distribution Generalization

no code implementations24 May 2022 Aishwarya Agrawal, Ivana Kajić, Emanuele Bugliarello, Elnaz Davoodi, Anita Gergely, Phil Blunsom, Aida Nematzadeh

Vision-and-language (V&L) models pretrained on large-scale multimodal data have demonstrated strong performance on various tasks such as image captioning and visual question answering (VQA).

Image Captioning Out-of-Distribution Generalization +3

Generating Diverse Programs with Instruction Conditioned Reinforced Adversarial Learning

no code implementations3 Dec 2018 Aishwarya Agrawal, Mateusz Malinowski, Felix Hill, Ali Eslami, Oriol Vinyals, tejas kulkarni

In this work, we study the setting in which an agent must learn to generate programs for diverse scenes conditioned on a given symbolic instruction.

Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering

1 code implementation CVPR 2018 Aishwarya Agrawal, Dhruv Batra, Devi Parikh, Aniruddha Kembhavi

Specifically, we present new splits of the VQA v1 and VQA v2 datasets, which we call Visual Question Answering under Changing Priors (VQA-CP v1 and VQA-CP v2 respectively).

Question Answering Visual Question Answering

C-VQA: A Compositional Split of the Visual Question Answering (VQA) v1.0 Dataset

no code implementations26 Apr 2017 Aishwarya Agrawal, Aniruddha Kembhavi, Dhruv Batra, Devi Parikh

Finally, we evaluate several existing VQA models under this new setting and show that the performances of these models degrade by a significant amount compared to the original VQA setting.

Question Answering Visual Question Answering

Analyzing the Behavior of Visual Question Answering Models

1 code implementation EMNLP 2016 Aishwarya Agrawal, Dhruv Batra, Devi Parikh

Recently, a number of deep-learning based models have been proposed for the task of Visual Question Answering (VQA).

Question Answering Visual Question Answering

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