Search Results for author: Anahita Bhiwandiwalla

Found 12 papers, 5 papers with code

Uncovering Bias in Large Vision-Language Models at Scale with Counterfactuals

no code implementations30 May 2024 Phillip Howard, Kathleen C. Fraser, Anahita Bhiwandiwalla, Svetlana Kiritchenko

To address this challenging problem, we conduct a large-scale study of text generated by different LVLMs under counterfactual changes to input images.

counterfactual Question Answering +1

Uncovering Bias in Large Vision-Language Models with Counterfactuals

no code implementations29 Mar 2024 Phillip Howard, Anahita Bhiwandiwalla, Kathleen C. Fraser, Svetlana Kiritchenko

We comprehensively evaluate the text produced by different LVLMs under this counterfactual generation setting and find that social attributes such as race, gender, and physical characteristics depicted in input images can significantly influence toxicity and the generation of competency-associated words.

counterfactual Question Answering +1

SocialCounterfactuals: Probing and Mitigating Intersectional Social Biases in Vision-Language Models with Counterfactual Examples

1 code implementation CVPR 2024 Phillip Howard, Avinash Madasu, Tiep Le, Gustavo Lujan Moreno, Anahita Bhiwandiwalla, Vasudev Lal

Our approach utilizes Stable Diffusion with cross attention control to produce sets of counterfactual image-text pairs that are highly similar in their depiction of a subject (e. g., a given occupation) while differing only in their depiction of intersectional social attributes (e. g., race & gender).


Analyzing Zero-Shot Abilities of Vision-Language Models on Video Understanding Tasks

1 code implementation7 Oct 2023 Avinash Madasu, Anahita Bhiwandiwalla, Vasudev Lal

We investigate 9 foundational image-text models on a diverse set of video tasks that include video action recognition (video AR), video retrieval (video RT), video question answering (video QA), video multiple choice (video MC) and video captioning (video CP).

Action Recognition Multiple-choice +6

ManagerTower: Aggregating the Insights of Uni-Modal Experts for Vision-Language Representation Learning

1 code implementation31 May 2023 Xiao Xu, Bei Li, Chenfei Wu, Shao-Yen Tseng, Anahita Bhiwandiwalla, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan

With only 4M VLP data, ManagerTower achieves superior performances on various downstream VL tasks, especially 79. 15% accuracy on VQAv2 Test-Std, 86. 56% IR@1 and 95. 64% TR@1 on Flickr30K.

Representation Learning

Correlation of Auroral Dynamics and GNSS Scintillation with an Autoencoder

no code implementations4 Oct 2019 Kara Lamb, Garima Malhotra, Athanasios Vlontzos, Edward Wagstaff, Atılım Günes Baydin, Anahita Bhiwandiwalla, Yarin Gal, Alfredo Kalaitzis, Anthony Reina, Asti Bhatt

High energy particles originating from solar activity travel along the the Earth's magnetic field and interact with the atmosphere around the higher latitudes.

Prediction of GNSS Phase Scintillations: A Machine Learning Approach

no code implementations3 Oct 2019 Kara Lamb, Garima Malhotra, Athanasios Vlontzos, Edward Wagstaff, Atılım Günes Baydin, Anahita Bhiwandiwalla, Yarin Gal, Alfredo Kalaitzis, Anthony Reina, Asti Bhatt

We propose a novel architecture and loss function to predict 1 hour in advance the magnitude of phase scintillations within a time window of plus-minus 5 minutes with state-of-the-art performance.

BIG-bench Machine Learning

Using Scene Graph Context to Improve Image Generation

no code implementations11 Jan 2019 Subarna Tripathi, Anahita Bhiwandiwalla, Alexei Bastidas, Hanlin Tang

Generating realistic images from scene graphs asks neural networks to be able to reason about object relationships and compositionality.

Image Generation from Scene Graphs Open-Ended Question Answering +1

Intel nGraph: An Intermediate Representation, Compiler, and Executor for Deep Learning

1 code implementation24 Jan 2018 Scott Cyphers, Arjun K. Bansal, Anahita Bhiwandiwalla, Jayaram Bobba, Matthew Brookhart, Avijit Chakraborty, Will Constable, Christian Convey, Leona Cook, Omar Kanawi, Robert Kimball, Jason Knight, Nikolay Korovaiko, Varun Kumar, Yixing Lao, Christopher R. Lishka, Jaikrishnan Menon, Jennifer Myers, Sandeep Aswath Narayana, Adam Procter, Tristan J. Webb

The current approach, which we call "direct optimization", requires deep changes within each framework to improve the training performance for each hardware backend (CPUs, GPUs, FPGAs, ASICs) and requires $\mathcal{O}(fp)$ effort; where $f$ is the number of frameworks and $p$ is the number of platforms.

graph partitioning Management +1

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