1.1k citations · 1.3k across the 33 of their papers we have counts for
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MoqaGPT : Zero-Shot Multi-modal Open-domain Question Answering with Large Language Model
Le Zhang, Yihong Wu, Fengran Mo +2
Multi-modal open-domain question answering typically requires evidence retrieval from databases across diverse modalities, such as images, tables, passages, etc. Even Large Languag…
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 Accuracy has been effective so far in the IID…
Investigating Prompting Techniques for Zero- and Few-Shot Visual Question Answering
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 contemporary Vision-Language Models (VLMs). Ce…
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 tasks such as zero-shot image classifica…
Measuring Progress in Fine-grained Vision-and-Language Understanding
Emanuele Bugliarello, Laurent Sartran, Aishwarya Agrawal +2
While pretraining on large-scale image-text data from the Web has facilitated rapid progress on many vision-and-language (V&L) tasks, recent work has demonstrated that pretrained m…
An Examination of the Robustness of Reference-Free Image Captioning Evaluation Metrics
Saba Ahmadi, Aishwarya Agrawal
Recently, reference-free metrics such as CLIPScore (Hessel et al., 2021), UMIC (Lee et al., 2021), and PAC-S (Sarto et al., 2023) have been proposed for automatic reference-free ev…