1 citations · 2 across the 14 of their papers we have counts for
16 papers
FairLens: Benchmarking Fairness in Vision-Language Models for High-Stakes Decision-Making
Vahid Reza Khazaie, Ahmed Y. Radwan, Shaina Raza
Vision-language models (VLMs) are increasingly used to make decisions from visual inputs. We introduce FAIRLENS, a benchmark and evaluation framework for measuring both the fairnes…
Bias in the Picture: Benchmarking VLMs with Social-Cue News Images and LLM-as-Judge Assessment
Aravind Narayanan, Vahid Reza Khazaie, Shaina Raza
Large vision-language models (VLMs) can jointly interpret images and text, but they are also prone to absorbing and reproducing harmful social stereotypes when visual cues such as…
LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation
Ananya Raval, Aravind Narayanan, Vahid Reza Khazaie +1
Large Multimodal Models (LMMs) are typically trained on vast corpora of image-text data but are often limited in linguistic coverage, leading to biased and unfair outputs across la…
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…
Advancing Medical Representation Learning Through High-Quality Data
Negin Baghbanzadeh, Adibvafa Fallahpour, Yasaman Parhizkar +8
Despite the growing scale of medical Vision-Language datasets, the impact of dataset quality on model performance remains under-explored. We introduce Open-PMC, a high-quality medi…
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…
ViLBias: Detecting and Reasoning about Bias in Multimodal Content
Shaina Raza, Caesar Saleh, Azib Farooq +11
Detecting bias in multimodal news requires models that reason over text--image pairs, not just classify text. In response, we present ViLBias, a VQA-style benchmark and framework f…
Enhancing Anomaly Detection Generalization through Knowledge Exposure: The Dual Effects of Augmentation
Mohammad Akhavan Anvari, Rojina Kashefi, Vahid Reza Khazaie +2
Anomaly detection involves identifying instances within a dataset that deviate from the norm and occur infrequently. Current benchmarks tend to favor methods biased towards low div…
Benchmarking Vision-Language Contrastive Methods for Medical Representation Learning
Shuvendu Roy, Yasaman Parhizkar, Franklin Ogidi +5
We perform a comprehensive benchmarking of contrastive frameworks for learning multimodal representations in the medical domain. Through this study, we aim to answer the following…
Can Generative Models Improve Self-Supervised Representation Learning?
Sana Ayromlou, Vahid Reza Khazaie, Fereshteh Forghani +1
The rapid advancement in self-supervised representation learning has highlighted its potential to leverage unlabeled data for learning rich visual representations. However, the exi…
Towards Realistic Out-of-Distribution Detection: A Novel Evaluation Framework for Improving Generalization in OOD Detection
Vahid Reza Khazaie, Anthony Wong, Mohammad Sabokrou
This paper presents a novel evaluation framework for Out-of-Distribution (OOD) detection that aims to assess the performance of machine learning models in more realistic settings.…
Augment to Detect Anomalies with Continuous Labelling
Vahid Reza Khazaie, Anthony Wong, Yalda Mohsenzadeh
Anomaly detection is to recognize samples that differ in some respect from the training observations. These samples which do not conform to the distribution of normal data are call…
Anomaly Detection with Adversarially Learned Perturbations of Latent Space
Vahid Reza Khazaie, Anthony Wong, John Taylor Jewell +1
Anomaly detection is to identify samples that do not conform to the distribution of the normal data. Due to the unavailability of anomalous data, training a supervised deep neural…
Latent Vector Recovery of Audio GANs
Andrew Keyes, Nicky Bayat, Vahid Reza Khazaie +1
Advanced Generative Adversarial Networks (GANs) are remarkable in generating intelligible audio from a random latent vector. In this paper, we examine the task of recovering the la…
Multi Scale Identity-Preserving Image-to-Image Translation Network for Low-Resolution Face Recognition
Vahid Reza Khazaie, Nicky Bayat, Yalda Mohsenzadeh
State-of-the-art deep neural network models have reached near perfect face recognition accuracy rates on controlled high-resolution face images. However, their performance is drast…
Inverse mapping of face GANs
Nicky Bayat, Vahid Reza Khazaie, Yalda Mohsenzadeh
Generative adversarial networks (GANs) synthesize realistic images from a random latent vector. While many studies have explored various training configurations and architectures f…