Zobrazeno 1 - 10
of 3 576
pro vyhledávání: '"Chen Yingying"'
Visual Anomaly Detection (VAD) aims to identify abnormal samples in images that deviate from normal patterns, covering multiple domains, including industrial, logical, and medical fields. Due to the domain gaps between these fields, existing VAD meth
Externí odkaz:
http://arxiv.org/abs/2412.03342
Sequential recommendation approaches have demonstrated remarkable proficiency in modeling user preferences. Nevertheless, they are susceptible to profile pollution attacks (PPA), wherein items are introduced into a user's interaction history delibera
Externí odkaz:
http://arxiv.org/abs/2412.01127
Overfitting has long been stigmatized as detrimental to model performance, especially in the context of anomaly detection. Our work challenges this conventional view by introducing a paradigm shift, recasting overfitting as a controllable and strateg
Externí odkaz:
http://arxiv.org/abs/2412.00560
Autor:
Vu, Tuan-Hung, Valle, Eduardo, Bursuc, Andrei, Kerssies, Tommie, de Geus, Daan, Dubbelman, Gijs, Qian, Long, Zhu, Bingke, Chen, Yingying, Tang, Ming, Wang, Jinqiao, Vojíř, Tomáš, Šochman, Jan, Matas, Jiří, Smith, Michael, Ferrie, Frank, Basu, Shamik, Sakaridis, Christos, Van Gool, Luc
We propose the unified BRAVO challenge to benchmark the reliability of semantic segmentation models under realistic perturbations and unknown out-of-distribution (OOD) scenarios. We define two categories of reliability: (1) semantic reliability, whic
Externí odkaz:
http://arxiv.org/abs/2409.15107
Pretrained vision-language models (VLMs), \eg CLIP, are increasingly used to bridge the gap between open- and close-vocabulary recognition in open-vocabulary image segmentation. As VLMs are generally pretrained with low-resolution images (e.g. $224\t
Externí odkaz:
http://arxiv.org/abs/2408.14776
Zero-shot anomaly detection (ZSAD) methods entail detecting anomalies directly without access to any known normal or abnormal samples within the target item categories. Existing approaches typically rely on the robust generalization capabilities of m
Externí odkaz:
http://arxiv.org/abs/2404.13671
Vision-Language Models (VLMs), such as CLIP, play a foundational role in various cross-modal applications. To fully leverage VLMs' potential in adapting to downstream tasks, context optimization methods like Prompt Tuning are essential. However, one
Externí odkaz:
http://arxiv.org/abs/2404.10357
Autor:
Li, Junze, Hu, Junchao, Luo, Ting, Chen, Dongliang, Chen, Yingying, Liu, Zeyi, Gao, Dingshan, Wen, Xinglin, Li, Dehui
Active optical waveguides combine light source and waveguides together in an individual component, which are essential for the integrated photonic chips. Although 1D luminescent materials based optical waveguides were extensively investigated, 2D wav
Externí odkaz:
http://arxiv.org/abs/2404.05138
Autor:
Sui, Yang, Phan, Huy, Xiao, Jinqi, Zhang, Tianfang, Tang, Zijie, Shi, Cong, Wang, Yan, Chen, Yingying, Yuan, Bo
In the exciting generative AI era, the diffusion model has emerged as a very powerful and widely adopted content generation and editing tool for various data modalities, making the study of their potential security risks very necessary and critical.
Externí odkaz:
http://arxiv.org/abs/2402.02739
Physical layer key generation based on reciprocal and random wireless channels has been an attractive solution for securing resource-constrained low-power wide-area networks (LPWANs). When quantizing channel measurements, namely received signal stren
Externí odkaz:
http://arxiv.org/abs/2310.07853