Mind the Context: The Impact of Contextualization in Neural Module Networks for Grounding Visual Referring Expressions

no code implementations • EMNLP 2021 • Arjun Akula, Spandana Gella, Keze Wang, Song-Chun Zhu, Siva Reddy

Our model outperforms the state-of-the-art NMN model on CLEVR-Ref+ dataset with +8. 1% improvement in accuracy on the single-referent test set and +4. 3% on the full test set.

CrossVQA: Scalably Generating Benchmarks for Systematically Testing VQA Generalization

no code implementations • EMNLP 2021 • Arjun Akula, Soravit Changpinyo, Boqing Gong, Piyush Sharma, Song-Chun Zhu, Radu Soricut

One challenge in evaluating visual question answering (VQA) models in the cross-dataset adaptation setting is that the distribution shifts are multi-modal, making it difficult to identify if it is the shifts in visual or language features that play a key role.

Answer Generation Question-Answer-Generation +3

Robust Visual Reasoning via Language Guided Neural Module Networks

no code implementations • NeurIPS 2021 • Arjun Akula, Varun Jampani, Soravit Changpinyo, Song-Chun Zhu

Neural module networks (NMN) are a popular approach for solving multi-modal tasks such as visual question answering (VQA) and visual referring expression recognition (REF).

Question Answering Referring Expression +3

Classification of Attributes in a Natural Language Query into Different SQL Clauses

no code implementations • RANLP 2015 • Ashish Palakurthi, Ruthu S M, Arjun Akula, Radhika Mamidi

General Classification Question Answering

A Novel Approach Towards Incorporating Context Processing Capabilities in NLIDB System

no code implementations • IJCNLP 2013 • Arjun Akula, Rajeev Sangal, Radhika Mamidi

Question Answering