Aishwarya Agrawal

Biography

Aishwarya Agrawal is an assistant professor in the Department of Computer Science and Operations Research at Université de Montréal, a Canada CIFAR AI Chair, and a core academic member of Mila – Quebec Artificial Intelligence Institute.

Agrawal also works as a research scientist one day a week at DeepMind. Previously, she held this position full time (August 2019 to December 2020). She completed her PhD in August 2019 at Georgia Tech, where she worked with Dhruv Batra and Devi Parikh.

Her research interests lie at the intersection of the following sub-disciplines of AI: computer vision, deep learning and natural language processing. The focus is developing AI systems that can ‘see’ (i.e., understand the contents of an image: who, what, where, doing what?) and ‘talk’ (i.e., communicate the understanding to humans in free-form natural language).

Aishwarya has received many awards and scholarships: Georgia Tech 2020 Sigma Xi Best PhD Thesis Award, 2020 Georgia Tech College of Computing Dissertation Award, 2019 Google Fellowship (declined due to graduation), 2019–2020 Facebook Fellowship (declined due to graduation) and 2018–2019 NVIDIA Graduate Fellowship. She was one of two runners-up in the 2019 AAAI/ACM SIGAI Dissertation Award, and was selected as a 2018 Rising Star in EECS.

She holds a bachelor's degree in electrical engineering with a minor in computer science and engineering from the Indian Institute of Technology Gandhinagar (2014).

Current Students

Saba Ahmadi

Master's Research - Université de Montréal

Rabiul Awal

PhD - Université de Montréal

awalrabiul6@gmail.com

Collaborating researcher - University of British Columbia

ankursikarwardc@gmail.com

Kanishk Jain

PhD - Université de Montréal

PhD - Université de Montréal

Master's Research - Université de Montréal

Master's Research - Université de Montréal

Qian Yang

PhD - Université de Montréal

Le Zhang

PhD - Université de Montréal

Publications

Benchmarking Vision Language Models for Cultural Understanding

Shravan Nayak

Kanishk Jain

Rabiul Awal

Siva Reddy

Sjoerd van Steenkiste

Lisa Anne Hendricks

Karolina Sta'nczak

Foundation models and vision-language pre-training have notably advanced Vision Language Models (VLMs), enabling multimodal processing of vi… (see more)sual and linguistic data. However, their performance has been typically assessed on general scene understanding - recognizing objects, attributes, and actions - rather than cultural comprehension. This study introduces CulturalVQA, a visual question-answering benchmark aimed at assessing VLM's geo-diverse cultural understanding. We curate a collection of 2,378 image-question pairs with 1-5 answers per question representing cultures from 11 countries across 5 continents. The questions probe understanding of various facets of culture such as clothing, food, drinks, rituals, and traditions. Benchmarking VLMs on CulturalVQA, including GPT-4V and Gemini, reveals disparity in their level of cultural understanding across regions, with strong cultural understanding capabilities for North America while significantly lower performance for Africa. We observe disparity in their performance across cultural facets too, with clothing, rituals, and traditions seeing higher performances than food and drink. These disparities help us identify areas where VLMs lack cultural understanding and demonstrate the potential of CulturalVQA as a comprehensive evaluation set for gauging VLM progress in understanding diverse cultures.

2024-01-01

EMNLP (published)

MoqaGPT : Zero-Shot Multi-modal Open-domain Question Answering with Large Language Model

Le Zhang

Yihong Wu

Fengran Mo

Jian-Yun Nie

Multi-modal open-domain question answering typically requires evidence retrieval from databases across diverse modalities, such as images, t… (see more)ables, passages, etc. Even Large Language Models (LLMs) like GPT-4 fall short in this task. To enable LLMs to tackle the task in a zero-shot manner, we introduce MoqaGPT, a straightforward and flexible framework. Using a divide-and-conquer strategy that bypasses intricate multi-modality ranking, our framework can accommodate new modalities and seamlessly transition to new models for the task. Built upon LLMs, MoqaGPT retrieves and extracts answers from each modality separately, then fuses this multi-modal information using LLMs to produce a final answer. Our methodology boosts performance on the MMCoQA dataset, improving F1 by +37.91 points and EM by +34.07 points over the supervised baseline. On the MultiModalQA dataset, MoqaGPT surpasses the zero-shot baseline, improving F1 by 9.5 points and EM by 10.1 points, and significantly closes the gap with supervised methods. Our codebase is available at https://github.com/lezhang7/MOQAGPT.

2023-10-07

EMNLP/2023/Conference (published)

openreview.net

Investigating Prompting Techniques for Zero- and Few-Shot Visual Question Answering

Rabiul Awal

Le Zhang

In this paper, we explore effective prompting techniques to enhance zero- and few-shot Visual Question Answering (VQA) performance in contem… (see more)porary Vision-Language Models (VLMs). Central to our investigation is the role of question templates in guiding VLMs to generate accurate answers. We identify that specific templates significantly influence VQA outcomes, underscoring the need for strategic template selection. Another pivotal aspect of our study is augmenting VLMs with image captions, providing them with additional visual cues alongside direct image features in VQA tasks. Surprisingly, this augmentation significantly improves the VLMs' performance in many cases, even though VLMs"see"the image directly! We explore chain-of-thought (CoT) reasoning and find that while standard CoT reasoning causes drops in performance, advanced methods like self-consistency can help recover it. Furthermore, we find that text-only few-shot examples enhance VLMs' alignment with the task format, particularly benefiting models prone to verbose zero-shot answers. Lastly, to mitigate the challenges associated with evaluating free-form open-ended VQA responses using string-matching based VQA metrics, we introduce a straightforward LLM-guided pre-processing technique to adapt the model responses to the expected ground-truth answer distribution. In summary, our research sheds light on the intricacies of prompting strategies in VLMs for VQA, emphasizing the synergistic use of captions, templates, and pre-processing to enhance model efficacy.

2023-06-16

ArXiv (preprint)

An Examination of the Robustness of Reference-Free Image Captioning Evaluation Metrics

Saba Ahmadi

2023-05-24

ArXiv (preprint)

MAPL: Parameter-Efficient Adaptation of Unimodal Pre-Trained Models for Vision-Language Few-Shot Prompting

Oscar Mañas

Pau Rodriguez

Saba Ahmadi

Aida Nematzadeh

Yash Goyal

Large pre-trained models have proved to be remarkable zero- and (prompt-based) few-shot learners in unimodal vision and language tasks. We p… (see more)ropose MAPL, a simple and parameter-efficient method that reuses frozen pre-trained unimodal models and leverages their strong generalization capabilities in multimodal vision-language (VL) settings. MAPL learns a lightweight mapping between the representation spaces of unimodal models using aligned image-text data, and can generalize to unseen VL tasks from just a few in-context examples. The small number of trainable parameters makes MAPL effective at low-data and in-domain learning. Moreover, MAPL’s modularity enables easy extension to other pre-trained models. Extensive experiments on several visual question answering and image captioning benchmarks show that MAPL achieves superior or competitive performance compared to similar methods while training orders of magnitude fewer parameters. MAPL can be trained in just a few hours using modest computational resources and public datasets. We release our code and pre-trained model weights at https://github.com/oscmansan/mapl.

2023-05-01

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (published)

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

Ivana Kaji'c

Emanuele Bugliarello

Elnaz Davoodi

Anita Gergely

Phil Blunsom

Aida Nematzadeh

2023-05-01

Findings of the Association for Computational Linguistics: EACL 2023 (published)

Contrasting Intra-Modal and Ranking Cross-Modal Hard Negatives to Enhance Visio-Linguistic Fine-grained Understanding

Le Zhang

Rabiul Awal

2023-01-01

arXiv.org (preprint)

Measuring Progress in Fine-grained Vision-and-Language Understanding

Emanuele Bugliarello

Laurent Sartran

Lisa Anne Hendricks

Aida Nematzadeh

While pretraining on large-scale image–text data from the Web has facilitated rapid progress on many vision-and-language (V&L) tasks, rece… (see more)nt work has demonstrated that pretrained models lack “fine-grained” understanding, such as the ability to recognise relationships, verbs, and numbers in images. This has resulted in an increased interest in the community to either develop new benchmarks or models for such capabilities. To better understand and quantify progress in this direction, we investigate four competitive V&L models on four fine-grained benchmarks. Through our analysis, we find that X-VLM (Zeng et al., 2022) consistently outperforms other baselines, and that modelling innovations can impact performance more than scaling Web data, which even degrades performance sometimes. Through a deeper investigation of X-VLM, we highlight the importance of both novel losses and rich data sources for learning fine-grained skills. Finally, we inspect training dynamics, and discover that for some tasks, performance peaks early in training or significantly fluctuates, never converging.

2023-01-01

ACL (1) (published)

Vision-Language Pretraining: Current Trends and the Future

Damien Teney

Aida Nematzadeh

In the last few years, there has been an increased interest in building multimodal (vision-language) models that are pretrained on larger bu… (see more)t noisier datasets where the two modalities (e.g., image and text) loosely correspond to each other (e.g., Lu et al., 2019; Radford et al., 2021). Given a task (such as visual question answering), these models are then often fine-tuned on task-specific supervised datasets. (e.g., Lu et al., 2019; Chen et al.,2020; Tan and Bansal, 2019; Li et al., 2020a,b). 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). In this tutorial, we focus on recent vision-language pretraining paradigms. Our goal is to first provide the background on image–language datasets, benchmarks, and modeling innovations before the multimodal pretraining area. Next we discuss the different family of models used for vision-language pretraining, highlighting their strengths and shortcomings. Finally, we discuss the limits of vision-language pretraining through statistical learning, and the need for alternative approaches such as causal representation learning.

2022-01-01

Annual Meeting of the Association for Computational Linguistics (published)

Vision-Language Pretraining: Current Trends and the Future

Damien Teney

Aida Nematzadeh

2022-01-01

Annual Meeting of the Association for Computational Linguistics (published)