5 citations · 11 across the 10 of their papers we have counts for
8 papers · 1 filter
Beyond Content: How Grammatical Gender Shapes Visual Representation in Text-to-Image Models
Muhammed Saeed, Shaina Raza, Ashmal Vayani +3
Research on bias in Text-to-Image (T2I) models has primarily focused on demographic representation and stereotypical attributes, overlooking a fundamental question: how does gramma…
Thinking Beyond Tokens: From Brain-Inspired Intelligence to Cognitive Foundations for Artificial General Intelligence and its Societal Impact
Rizwan Qureshi, Ranjan Sapkota, Abbas Shah +16
Can machines truly think, reason and act in domains like humans? This enduring question continues to shape the pursuit of Artificial General Intelligence (AGI). Despite the growing…
A Culturally-diverse Multilingual Multimodal Video Benchmark & Model
Bhuiyan Sanjid Shafique, Ashmal Vayani, Muhammad Maaz +26
Large multimodal models (LMMs) have recently gained attention due to their effectiveness to understand and generate descriptions of visual content. Most existing LMMs are in Englis…
HumaniBench: A Human-Centric Framework for Large Multimodal Models Evaluation
Shaina Raza, Aravind Narayanan, Vahid Reza Khazaie +6
Although recent large multimodal models (LMMs) show impressive progress on vision language tasks, their alignment with human centered (HC) principles such as fairness, ethics, incl…
GAEA: A Geolocation Aware Conversational Assistant
Ron Campos, Ashmal Vayani, Parth Parag Kulkarni +4
Image geolocalization, in which an AI model traditionally predicts the precise GPS coordinates of an image, is a challenging task with many downstream applications. However, the us…
VLDBench Evaluating Multimodal Disinformation with Regulatory Alignment
Shaina Raza, Ashmal Vayani, Aditya Jain +8
Detecting disinformation that blends manipulated text and images has become increasingly challenging, as AI tools make synthetic content easy to generate and disseminate. While mos…
BBQ-V: Benchmarking Visual Stereotype Bias in Large Multimodal Models
Vishal Narnaware, Ashmal Vayani, Rohit Gupta +2
Stereotype biases in Large Multimodal Models (LMMs) perpetuate harmful societal prejudices, undermining the fairness and equity of AI applications. As LMMs grow increasingly influe…
Who is Responsible? The Data, Models, Users or Regulations? A Comprehensive Survey on Responsible Generative AI for a Sustainable Future
Shaina Raza, Rizwan Qureshi, Anam Zahid +14
Generative AI is rapidly moving from research to deployment, elevating the need for responsible development, evaluation, and governance. We conduct a PRISMA guided review of 232 st…