Machine unlearning (MU) is gaining increasing attention due to the need to remove or
modify predictions made by machine learning (ML) models. While training models have …

Human skeleton-based action recognition offers a valuable means to understand the
intricacies of human behavior because it can handle the complex relationships between …

In this paper, we leverage CLIP for zero-shot sketch based image retrieval (ZS-SBIR). We
are largely inspired by recent advances on foundation models and the unparalleled …

Data sparsity is a long-standing problem in recommender systems. To alleviate it, Cross-
Domain Recommendation (CDR) has attracted a surge of interests, which utilizes the rich …

Recommender systems have been widely deployed in many real-world applications, but
usually suffer from the long-standing user cold-start problem. As a promising way, Cross …

In a wide range of multimodal tasks, contrastive learning has become a particularly
appealing approach since it can successfully learn representations from abundant …

This paper for the first time explores text-to-image diffusion models for Zero-Shot Sketch-
based Image Retrieval (ZS-SBIR). We highlight a pivotal discovery: the capacity of text-to …

Abstract Multi-View Representation Learning (MVRL) aims to discover a shared
representation of observations from different views with the complex underlying correlation …

In this paper, we study the zero-shot sketch-based image retrieval (ZS-SBIR) task, which
retrieves natural images related to sketch queries from unseen categories. In the literature …

Language features are evolving in real-world social media, resulting in the deteriorating
performance of text classification in dynamics. To address this challenge, we study temporal …