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20202023
most citedCalFAT: Calibrated Federated Adversarial Training with Label Skewness

15 citations · 23 across the 9 of their papers we have counts for

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5 papers · 1 filter

cs.CV2023

DIAGNOSIS: Detecting Unauthorized Data Usages in Text-to-image Diffusion Models

Zhenting Wang, Chen Chen, Lingjuan Lyu +2

Recent text-to-image diffusion models have shown surprising performance in generating high-quality images. However, concerns have arisen regarding the unauthorized data usage durin…

cs.CV2023

Alteration-free and Model-agnostic Origin Attribution of Generated Images

Zhenting Wang, Chen Chen, Yi Zeng +2

Recently, there has been a growing attention in image generation models. However, concerns have emerged regarding potential misuse and intellectual property (IP) infringement assoc…

cs.CV2022

Context Label Learning: Improving Background Class Representations in Semantic Segmentation

Zeju Li, Konstantinos Kamnitsas, Cheng Ouyang +2

Background samples provide key contextual information for segmenting regions of interest (ROIs). However, they always cover a diverse set of structures, causing difficulties for th…

cs.CV2022

Estimating Model Performance under Domain Shifts with Class-Specific Confidence Scores

Zeju Li, Konstantinos Kamnitsas, Mobarakol Islam +2

Machine learning models are typically deployed in a test setting that differs from the training setting, potentially leading to decreased model performance because of domain shift.…

cs.CV2020

Towards a category-extended object detector with limited data

Bowen Zhao, Chen Chen, Xi Xiao +1

Object detectors are typically learned on fully-annotated training data with fixed predefined categories. However, categories are often required to be increased progressively. Usua…