Abstract Machine unlearning has become a pivotal task to erase the influence of data from a
trained model. It adheres to recent data regulation standards and enhances the privacy and …

Benchmarks and leaderboards are commonly used to track the fairness impacts of artificial
intelligence (AI) models. Many critics argue against this practice, since it incentivizes …

Large vision-language models (LVLMs) have recently achieved significant progress,
demonstrating strong capabilities in open-world visual understanding. However, it is not yet …

Recent dataset deduplication techniques have demonstrated that content-aware dataset
pruning can dramatically reduce the cost of training Vision-Language Pretrained (VLP) …

Federated Learning (FL) is a distributed learning paradigm that enhances users' privacy by
eliminating the need for clients to share raw, private data with the server. Despite the …

Abstract Facial Attribute Classification (FAC) holds substantial promise in widespread
applications. However FAC models trained by traditional methodologies can be unfair by …

Learning fair representation in deep learning is essential to mitigate discriminatory
outcomes and enhance trustworthiness. However previous research has been commonly …

We present a method for prediction of a person's hairstyle from a single image. Despite
growing use cases in user digitization and enrollment for virtual experiences, available …

Recent studies have highlighted the issue of stereotypical depictions for people of different
identity groups in Text-to-Image (T2I) model generations. However, these existing …

The deployment of autonomous vehicles (AVs) is rapidly expanding to numerous cities. At
the heart of AVs, the object detection module assumes a paramount role, directly influencing …