(NeurIPS 2024)Text-Guided Attention is All You Need for Zero-Shot Robustness in Vision-Language Models adversarial-robustness zero-shot-adversarial-robustness Updated Nov 14, 2024 Python Improve this page Add a description, image, and links to the zero-shot-adversarial-robustness topic page so ...
Zero-Shot Adversarial Robustness LVM模型中对于新的任务和数据集巨有良好的泛化性,CLIP在zero-shot任务上的能力显著。但是,尚未有人关注过zero-shot的对抗鲁棒性的迁移效果。本文主要针对的就是在zero-shot任务上模型的对抗鲁棒性能力。 Adversarial Training 对抗训练是一种普遍的用于增强模型对抗鲁棒性的方式,其原理...
Zero-shot learningRobust generalizationAdversarial robustnessData shift robustness is an active research topic, however, it has been primarily investigated from a fully supervised perspective, and robustness of zero-shot learning (ZSL) models have been largely neglected. In this paper, we present a ...
其并不能反映真正的zero-shot场景。故创建一个明确的新任务基准，以评估广泛的zero-shot迁移能力，而不...
we have observed a phenomenon wherein adversarial perturbations induce shifts in text-guided attention. Building upon this observation, we propose a simple yet effective strategy:Text-Guided Attention for Zero-Shot Robustness (TGA-ZSR). This framework incorporates two components: the Attention Refinement...
Here, we present a zero-shot deconvolution deep neural network (ZS-DeconvNet) framework that is able to train a DLSR network in an unsupervised manner using as few as only one single planar image or volumetric image stack of low-resolution and low-SNR, which results in a zero-shot implemen...
However, an important caveat is that these robustness improvements are largest in the zero-shot setting, i.e., when the model per- forms inference without fine-tuning on a target distribution. In a concrete application, a zero-shot model can be fine-tuned on extra application-specific data,...
论文关键词:Zero-shot learning,Robust generalization,Adversarial robustness论文评审过程:Received 26 September 2021, Revised 4 December 2021, Accepted 18 January 2022, Available online 23 January 2022, Version of Record 1 February 2022.论文官网地址:https://doi.org/10.1016/j.imavis.2022.104392 ...
A survey of zero-shot learning: settings, methods, and applications ACM Trans. Intell. Syst. Technol., 10 (2) (2019), pp. 1-37 Google Scholar Won et al., 2022 Dong-Ok Won, Yong-Nam Jang, Seong-Whan Lee Plausmal-gan: plausible malware training based on generative adversarial networks...
Speaker embedding based zero-shot Text-to-Speech (TTS) systems enable high-quality speech synthesis for unseen speakers using minimal data. However, these systems are vulnerable to adversarial attacks, where an attacker introduces imperceptible perturbations to the original speaker's audio waveform, ...