Abstract We introduce Diff-Tracker, a novel approach for the challenging unsupervised
visual tracking task leveraging the pre-trained text-to-image diffusion model. Our main idea …

The performance of deep learning models is intrinsically tied to the quality volume and
relevance of their training data. Gathering ample data for production scenarios often …

Large-scale diffusion models are adept at generating high-fidelity images and facilitating
image editing and interpolation. However, they have limitations when tasked with generating …

Text-to-Image (T2I) models have raised security concerns due to their potential to generate
inappropriate or harmful images. In this paper, we propose UPAM, a novel framework that …

Domain adaptation is a method used to reduce discrepancy between source and target
domains and to enhance generalization performance by transforming the distribution of …

Human Activity Recognition (HAR) plays a crucial role in various applications such as
human-computer interaction and healthcare monitoring. However, challenges persist in …

Unsupervised Domain Adaptation (UDA) aims to adapt a model trained on a labeled source
domain to an unlabeled target domain by addressing the domain shift. Existing …

요 약최근 레이블이 불충분한 새로운 데이터가 끊임없이 생성되면서 이러한 데이터를 어떻게
학습할 것인가에 대한 연구가 활발히 진행되고 있다. 그 중에서도 도메인 적응 (Domain …