Question Answering
Overcoming Language Priors in Visual Question Answering with Adversarial Regularization
Sainandan Ramakrishnan, Aishwarya Agrawal, Stefan Lee
Neural Information Processing Systems http://nips.cc/
Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions
Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, Joris M. Mooij
Neural Information Processing Systems http://nips.cc/
Chain of Reasoning for Visual Question Answering
Chenfei Wu, Jinlai Liu, Xiaojie Wang, Xuan Dong
Neural Information Processing Systems http://nips.cc/
RUBi: Reducing Unimodal Biases for Visual Question Answering
Remi Cadene, Corentin Dancette, Hedi Ben younes, Matthieu Cord, Devi Parikh
Human Attentionin Answering: Do Humansand Deep Networks Lookatthe Same Regions?
From Visual Question Answering to multimodal learning: an interview with Aishwarya Agrawal
You were awarded an Honourable Mention for the 2019 AAAI / ACM SIGAI Doctoral Dissertation Award. What was the topic of your dissertation research, and what were the main contributions or findings? My PhD dissertation was on the topic of Visual Question Answering, called VQA. We proposed the task of open-ended and free-form VQA - a new way to benchmark computer vision models by asking them questions about images. We curated a large-scale dataset for researchers to train and test their models on this task.
Fine-grained Late-interaction Multi-modal Retrieval for Retrieval Augmented Visual Question Answering (Appendix)
We chose the Google Search corpus [Luo et al., 2021] for our question-answering system as it provides good coverage of the knowledge needed and is publicly available. Therefore, it is advised to conduct an ethical review prior to deploying the system in live service. Table 1 shows the data statistics of the OK-VQA dataset. We build a DPR retriever as a baseline for FLMR. Equally contributed as the first author 37th Conference on Neural Information Processing Systems (NeurIPS 2023). The inner product search (supported by FAISS [Johnson et al., 2019]) is used to train and In answer generation, we use t5-large and Salesforce/blip2-flan-t5-xl.
Fine-grained Late-interaction Multi-modal Retrieval for Retrieval Augmented Visual Question Answering
Retrieval (DPR) and then uses them to answer questions.
e43739bba7cdb577e9e3e4e42447f5a5-AuthorFeedback.pdf
R3 brings up an important question whether BetaE is fully36 expressive and can model any given query-answer pairs on a KG. This is a challenging problem and our ongoing37 researchconcern.