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Gerald Schaefer

9 accepted papers

2026

ACID-Style: An Adaptive Condition Injection Diffusion Model for Arbitrary Style Transfer

AAAI 2026technical

Arbitrary style transfer (AST), a popular AI-powered photo editing function, aims to strike an optimal balance between content and style injection from two images in order to generate a novel high-fidelity stylised image. Recently, diffusion models have been applied to AST due to their high generati

Cited by 0SourcePDFScholar
2025

Recoverable Facial Identity Protection via Adaptive Makeup Transfer Adversarial Attacks

AAAI 2025technical

Unauthorised face recognition (FR) systems have posed significant threats to digital identity and privacy protection. To alleviate the risk of compromised identities, recent makeup transfer-based attack methods embed adversarial signals in order to confuse unauthorised FR systems. However, their maj…

2024

HPL-ESS: Hybrid Pseudo-Labeling for Unsupervised Event-based Semantic Segmentation

CVPR 2024poster

Event-based semantic segmentation has gained popularity due to its capability to deal with scenarios under high-speed motion and extreme lighting conditions which cannot be addressed by conventional RGB cameras. Since it is hard to annotate event data previous approaches rely on event-to-image recon…

Cited by 5SourcePDFScholar
2024

Towards compact reversible image representations for neural style transfer

ECCV 2024poster

"Arbitrary neural style transfer aims to stylise a content image by referencing a provided style image. Despite various efforts to achieve both content preservation and style transferability, learning effective representations for this task remains challenging since the redundancy of content and sty…

Cited by 0SourcePDFScholar
2023

Gradient-Based Graph Attention for Scene Text Image Super-resolution

AAAI 2023technical

Scene text image super-resolution (STISR) in the wild has been shown to be beneficial to support improved vision-based text recognition from low-resolution imagery. An intuitive way to enhance STISR performance is to explore the well-structured and repetitive layout characteristics of text and explo…

2023

Robust Steganography without Embedding Based on Secure Container Synthesis and Iterative Message Recovery

IJCAI 2023poster

Synthesis-based steganography without embedding (SWE) methods transform secret messages to container images synthesised by generative networks, which eliminates distortions of container images and thus can fundamentally resist typical steganalysis tools. However, existing methods suffer from weak me…

Cited by 2SourcePDFScholar
2022

Image Disentanglement Autoencoder for Steganography Without Embedding

CVPR 2022poster

Conventional steganography approaches embed a secret message into a carrier for concealed communication but are prone to attack by recent advanced steganalysis tools. In this paper, we propose Image DisEntanglement Autoencoder for Steganography (IDEAS) as a novel steganography without embedding (SWE…

Cited by 80PDFcodeScholar
2019

Skin Lesion Classification Using Hybrid Deep Neural Networks

ICASSP 2019accepted

Skin cancer is one of the major types of cancers with an increasing incidence over the past decades. Accurately diagnosing skin lesions to discriminate between benign and malignant skin lesions is crucial to ensure appropriate patient treatment. While there are many computerised methods for skin les…

Cited by 0SourceScholar
2015

Increasing allocated tasks with a time minimization algorithm for a search and rescue scenario

ICRA 2015poster

Rescue missions require both speed to meet strict time constraints and maximum use of resources. This study presents a Task Swap Allocation (TSA) algorithm that increases vehicle allocation with respect to the state-of-the-art consensus-based bundle algorithm and one of its extensions, while meeting…

Cited by 24SourceScholar