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Discovering and Mitigating Biases in CLIP-based Image Editing
Md Mehrab Tanjim;Krishna Kumar Singh;Kushal Kafle;Ritwik Sinha;Garrison W. Cottrell
2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Year: 2024 | Conference Paper |
Cited by: Papers (3)
In recent years, the use of CLIP (Contrastive Language-Image Pre-Training) has become increasingly popular in a wide range of downstream applications, including zero-shot image classification and text-to-image synthesis. Despite being trained on a vast dataset, the CLIP model has been found to exhibit biases against certain protected attributes, such as gender and race. While previous research has...Show More
The growth of low-end hardware has led to a proliferation of ma-chine learning-based services in edge applications. These applications gather contextual information about users and provide some services, such as personalized offers, through a machine learning (ML) model. A growing practice has been to deploy such ML models on the user's device to reduce latency, maintain user privacy, and minimize...Show More
VADER: Video Alignment Differencing and Retrieval
Alexander Black;Simon Jenni;Tu Bui;Md. Mehrab Tanjim;Stefano Petrangeli;Ritwik Sinha;Viswanathan Swaminathan;John Collomosse
2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
We propose VADER, a spatio- temporal matching, alignment, and change summarization method to help fight misinformation spread via manipulated videos. VADER matches and coarsely aligns partial video fragments to candidate videos using a robust visual descriptor and scalable search over adaptively chunked video content. A transformer- based alignment module then refines the temporal localization of ...Show More
Video Manipulations Beyond Faces: A Dataset with Human-Machine Analysis
Trisha Mittal;Ritwik Sinha;Viswanathan Swaminathan;John Collomosse;Dinesh Manocha
2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)
Year: 2023 | Conference Paper |
Cited by: Papers (5)
As tools for content editing mature, and artificial intelligence (AI) based algorithms for synthesizing media grow, the presence of manipulated content across online media is increasing. This phenomenon causes the spread of misinformation, creating a greater need to distinguish between “real” and “manipulated” content. To this end, we present Videosham, a dataset consisting of 826 videos (413 real...Show More
Contextualized Styling of Images for Web Interfaces using Reinforcement Learning
Pooja Guhan;Saayan Mitra;Somdeb Sarkhel;Stefano Petrangeli;Ritwik Sinha;Viswanathan Swaminathan;Aniket Bera;Dinesh Manocha
2022 IEEE International Symposium on Multimedia (ISM)
Year: 2022 | Conference Paper |
Content personalization is one of the foundations of today’s digital marketing. Often the same image needs to be adapted for different design schemes for content that is created for different occasions, geographic locations or other aspects of the target population. We present a novel reinforcement learning (RL) based method for automatically stylizing images to complement the design scheme of med...Show More
Generating and Controlling Diversity in Image Search
Md Mehrab Tanjim;Ritwik Sinha;Krishna Kumar Singh;Sridhar Mahadevan;David Arbour;Moumita Sinha;Garrison W. Cottrell
2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Year: 2022 | Conference Paper |
Cited by: Papers (3)
In our society, generations of systemic biases have led to some professions being more common among certain genders and races. This bias is also reflected in image search on stock image repositories and search engines, e.g., a query like “male Asian administrative assistant” may produce limited results. The pursuit of a utopian world demands providing content users with an opportunity to present a...Show More
BOhance: Bayesian Optimization for Content Enhancement
Trisha Mittal;Vishy Swaminathan;Somdeb Sarkhel;Ritwik Sinha;David Arbour;Saayan Mitra;Dinesh Manocha
2021 IEEE International Symposium on Multimedia (ISM)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
We present BOhance, an efficient solution for optimizing digital content like images. Our approach enhances the standard and widely-used method for optimizing content, A/B testing, by using Bayesian Optimization. Our work effectively extends A/B testing in the continuous domain where A/B testing cannot efficiently test infinitely many variants. We test our approach on an image enhancement task whe...Show More
Bayesian Estimation of the Effect of Television Advertising on Web Metrics
2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA)
Year: 2020 | Conference Paper |
Cited by: Papers (1)
Aggregate advertising-presenting a single ad to large groups of individuals through traditional media such as television and print-presents a unique challenge to measuring efficacy because treatment and outcome are observed from two disparate sources (interaction and revenue realization). In this work, we propose a Bayesian model to estimate the impact of an ad on observable web metrics that are r...Show More
RAPID: Rapid and Precise Interpretable Decision Sets
Sunny Dhamnani;Dhruv Singal;Ritwik Sinha;Tharun Mohandoss;Manish Dash
2019 IEEE International Conference on Big Data (Big Data)
Year: 2019 | Conference Paper |
Cited by: Papers (4)
Interpretable Decision Sets (IDS) is an approach to building transparent and interpretable supervised machine learning models. Unfortunately, IDS does not scale to most commonly encountered big data sets. In this paper, we propose Rapid And Precise Interpretable Decision Sets (RAPID), a faster alternative to IDS. We use the existing formulation of decision set learning and propose a time-efficient...Show More
Saliency Prediction for Mobile User Interfaces
Prakhar Gupta;Shubh Gupta;Ajaykrishnan Jayagopal;Sourav Pal;Ritwik Sinha
2018 IEEE Winter Conference on Applications of Computer Vision (WACV)
Year: 2018 | Conference Paper |
Cited by: Papers (10)
We introduce models for saliency prediction for mobile user interfaces. A mobile interface may include elements like buttons and text in addition to natural images which enable performing a variety of tasks. Saliency in natural images is a well studied topic. However, given the difference in what constitutes a mobile interface, and the usage context of these devices, we postulate that saliency pre...Show More
Estimating the incremental effects of interactions for marketing attribution
2014 International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC2014)
Year: 2014 | Conference Paper |
Assigning credit to different marketing activities has long been an important but challenging goal for a marketer. With the advent of digital marketing, the marketer can now potentially record each interaction with a prospective customer. With this development it is possible to measure and assign credit for each marketing interaction. We propose an econometric model to estimate the true incrementa...Show More
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