Portrait of Aishwarya Agrawal

Aishwarya Agrawal

Core Academic Member
aishwarya.agrawal@mila.quebec
Canada CIFAR AI Chair
Assistant Professor, Université de Montréal, Department of Computer Science and Operations Research
Research Scientist, Google DeepMind, Montréal
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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

Kanishk Jain
PhD - Université de Montréal
kanishk.jain@mila.quebec
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Le Zhang
Master's Research - Université de Montréal
le.zhang@mila.quebec
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Md Rabiul Awal
Research Intern - Université de Montréal
rabiul.awal@mila.quebec
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Oscar Mañas
PhD - Université de Montréal
oscar.manas@mila.quebec
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Shravan Nayak
Master's Research - Université de Montréal
shravan.nayak@mila.quebec
Website
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Qian Yang
PhD - Université de Montréal
qian.yang@mila.quebec
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Saba Ahmadi
Master's Research - Université de Montréal
saba.ahmadi@mila.quebec
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Sarvjeet Ghotra
Master's Research - Université de Montréal
Co-supervisor :
Aaron Courville
sarvjeet-singh.ghotra@mila.quebec
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Uday Kapur
Professional Master's - Université de Montréal
udaykaran.kapur@mila.quebec
Yash Vadi
Professional Master's - Université de Montréal
yash.vadi@mila.quebec
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Publications

Improving Text-to-Image Consistency via Automatic Prompt Optimization
Oscar Mañas
Pietro Astolfi
Melissa Hall
Candace Ross
Jack Urbanek
Adina Williams
Aishwarya Agrawal
Adriana Romero Soriano
Michal Drozdzal
2024-03-26
ArXiv (preprint)
doi.org
arxiv.org
Improving Automatic VQA Evaluation Using Large Language Models
Oscar Mañas
Benno Krojer
Aishwarya Agrawal
8 years after the visual question answering (VQA) task was proposed, accuracy remains the primary metric for automatic evaluation. VQA Accur… (see more)acy has been effective so far in the IID evaluation setting. However, our community is undergoing a shift towards open-ended generative models and OOD evaluation. In this new paradigm, the existing VQA Accuracy metric is overly stringent and underestimates the performance of VQA systems. Thus, there is a need to develop more robust automatic VQA metrics that serve as a proxy for human judgment. In this work, we propose to leverage the in-context learning capabilities of instruction-tuned large language models (LLMs) to build a better VQA metric. We formulate VQA evaluation as an answer-rating task where the LLM is instructed to score the accuracy of a candidate answer given a set of reference answers. We demonstrate the proposed metric better correlates with human judgment compared to existing metrics across several VQA models and benchmarks. We hope wide adoption of our metric will contribute to better estimating the research progress on the VQA task. We plan to release the evaluation code and collected human judgments.
2024-03-24
Proceedings of the AAAI Conference on Artificial Intelligence (published)
doi.org
arxiv.org
MoqaGPT : Zero-Shot Multi-modal Open-domain Question Answering with Large Language Model
Le Zhang
Yihong Wu
Fengran Mo
Jian-Yun Nie
Aishwarya Agrawal
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)
doi.org
openreview.net
Investigating Prompting Techniques for Zero- and Few-Shot Visual Question Answering
Md. Rabiul Awal
Le Zhang
Aishwarya Agrawal
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)
doi.org
arxiv.org
Contrasting Intra-Modal and Ranking Cross-Modal Hard Negatives to Enhance Visio-Linguistic Compositional Understanding
Le Zhang
Rabiul Awal
Aishwarya Agrawal
Vision-Language Models (VLMs), such as CLIP, exhibit strong image-text comprehension abilities, facilitating advances in several downstream … (see more)tasks such as zero-shot image classification, image-text retrieval, and text-to-image generation. However, the compositional reasoning abilities of existing VLMs remains subpar. The root of this limitation lies in the inadequate alignment between the images and captions in the pretraining datasets. Additionally, the current contrastive learning objective fails to focus on fine-grained grounding components like relations, actions, and attributes, resulting in"bag-of-words"representations. We introduce a simple and effective method to improve compositional reasoning in VLMs. Our method better leverages available datasets by refining and expanding the standard image-text contrastive learning framework. Our approach does not require specific annotations and does not incur extra parameters. When integrated with CLIP, our technique yields notable improvement over state-of-the-art baselines across five vision-language compositional benchmarks. We open-source our code at https://github.com/lezhang7/Enhance-FineGrained.
2023-06-15
ArXiv (preprint)
arxiv.org
arxiv.org
An Examination of the Robustness of Reference-Free Image Captioning Evaluation Metrics
Saba Ahmadi
Aishwarya Agrawal
2023-05-24
ArXiv (preprint)
doi.org
arxiv.org
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
Aishwarya Agrawal
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)
doi.org
arxiv.org
Reassessing Evaluation Practices in Visual Question Answering: A Case Study on Out-of-Distribution Generalization
Aishwarya Agrawal
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)
doi.org
arxiv.org
Contrasting Intra-Modal and Ranking Cross-Modal Hard Negatives to Enhance Visio-Linguistic Fine-grained Understanding
Le Zhang
Md. Rabiul Awal
Aishwarya Agrawal
2023-01-01
arXiv.org (preprint)
doi.org
Measuring Progress in Fine-grained Vision-and-Language Understanding
Emanuele Bugliarello
Laurent Sartran
Aishwarya Agrawal
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)
doi.org
arxiv.org
Vision-Language Pretraining: Current Trends and the Future
Aishwarya Agrawal
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)
doi.org
Vision-Language Pretraining: Current Trends and the Future
Aishwarya Agrawal
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)
doi.org