Dec 1, 2017 · We propose a new setting for VQA where for every question type, train and test sets have different prior distributions of answers.

Jun 11, 2018 · Specifically, GVQA explicitly disentangles the recognition of visual concepts present in the image from the identification of plausible answer ...

Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering. Aishwarya Agrawal, Dhruv Batra, Devi Parikh, Aniruddha Kembhavi

Code for the Grounded Visual Question Answering (GVQA) model from the below paper: Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question ...

GVQA explicitly disentangles the recognition of visual concepts present in the image from the identification of plausible answer space for a given question, ...

A number of studies have found that today's Visual Question Answering (VQA) models are heavily driven by superficial correlations in the training data and ...

Jun 18, 2018 · 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 ...

Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering. Aishwarya Agrawal. 1∗. , Dhruv Batra. 1,2. , Devi Parikh. 1,2. , Aniruddha ...

A number of studies have found that today's Visual Question Answering (VQA) models are heavily driven by superficial correlations in the training data and ...

Mar 24, 2023 · Bibliographic details on Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering.