Zobrazeno 1 - 10
of 350
pro vyhledávání: '"Fu, En"'
Autor:
Yu, Yu-Chu, Huang, Chi-Pin, Chen, Jr-Jen, Chang, Kai-Po, Lai, Yung-Hsuan, Yang, Fu-En, Wang, Yu-Chiang Frank
Large-scale vision-language models (VLMs) have shown a strong zero-shot generalization capability on unseen-domain data. However, adapting pre-trained VLMs to a sequence of downstream tasks often leads to the forgetting of previously learned knowledg
Externí odkaz:
http://arxiv.org/abs/2403.09296
The problem of the Remaining Useful Life (RUL) prediction, aiming at providing an accurate estimate of the remaining time from the current predicting moment to the complete failure of the device, has gained significant attention from researchers in r
Externí odkaz:
http://arxiv.org/abs/2401.16462
Federated Learning (FL) is an emerging paradigm that enables multiple users to collaboratively train a robust model in a privacy-preserving manner without sharing their private data. Most existing approaches of FL only consider traditional single-lab
Externí odkaz:
http://arxiv.org/abs/2312.07165
Autor:
Huang, Chi-Pin, Chang, Kai-Po, Tsai, Chung-Ting, Lai, Yung-Hsuan, Yang, Fu-En, Wang, Yu-Chiang Frank
Concept erasure in text-to-image diffusion models aims to disable pre-trained diffusion models from generating images related to a target concept. To perform reliable concept erasure, the properties of robustness and locality are desirable. The forme
Externí odkaz:
http://arxiv.org/abs/2311.17717
We propose a sparse and privacy-enhanced representation for Human Pose Estimation (HPE). Given a perspective camera, we use a proprietary motion vector sensor(MVS) to extract an edge image and a two-directional motion vector image at each time frame.
Externí odkaz:
http://arxiv.org/abs/2309.09515
Federated learning (FL) emerges as a decentralized learning framework which trains models from multiple distributed clients without sharing their data to preserve privacy. Recently, large-scale pre-trained models (e.g., Vision Transformer) have shown
Externí odkaz:
http://arxiv.org/abs/2308.15367
We propose a content-based system for matching video and background music. The system aims to address the challenges in music recommendation for new users or new music give short-form videos. To this end, we propose a cross-modal framework VMCML that
Externí odkaz:
http://arxiv.org/abs/2303.12379
To understand how deep neural networks perform classification predictions, recent research attention has been focusing on developing techniques to offer desirable explanations. However, most existing methods cannot be easily applied for semantic segm
Externí odkaz:
http://arxiv.org/abs/2302.09561
Although significant progress has been made in face recognition, demographic bias still exists in face recognition systems. For instance, it usually happens that the face recognition performance for a certain demographic group is lower than the other
Externí odkaz:
http://arxiv.org/abs/2211.15181
Due to the rise of spherical cameras, monocular 360 depth estimation becomes an important technique for many applications (e.g., autonomous systems). Thus, state-of-the-art frameworks for monocular 360 depth estimation such as bi-projection fusion in
Externí odkaz:
http://arxiv.org/abs/2209.02952