Anti-Fake Vaccine: Safeguarding Privacy Against Face Swapping via Visual-Semantic Dual Degradation

Information Science, Computer Science, Artificial Intelligence,
privacy protection   face swapping   adversarial perturbation   deepfake   visual degradation   semantic degradation  

Deepfake and Facial Privacy Protection: Innovative Research on Anti-Fake Vaccine Background and Motivation In recent years, advancements in deepfake technology have posed severe threats to personal privacy and social security. Facial swapping, a typical application of deepfake technology, is widely used in filmmaking and computer games, but its ris...

Weakly Supervised Semantic Segmentation of Driving Scenes Based on Few Annotated Pixels and Point Clouds

Engineering, Transportation Engineering, Information Science, Computer Science,
weakly supervised   semantic segmentation   driving scenes   point cloud data   pseudo labels  

Few Annotated Pixels and Point Cloud Based Weakly Supervised Semantic Segmentation of Driving Scenes Background and Research Issues Semantic segmentation, a critical task in computer vision, has extensive applications in domains like autonomous driving. However, traditional fully-supervised semantic segmentation methods require exhaustive pixel-lev...

Rethinking Contemporary Deep Learning Techniques for Error Correction in Biometric Data

Engineering, Electrical Engineering, Information Science, Computer Science, Artificial Intelligence, Life Sciences, Bioengineering,
biometrics   secure sketch   error correction   maximum likelihood decoding   deep learning  

Rethinking Deep Learning Techniques for Error Correction in Biometric Data Background With the rapid development of information technology, biometric data has become increasingly important in identity verification and secure storage. Traditional cryptography relies on uniformly distributed and precisely reproducible random strings. However, most re...

Day2Dark: Pseudo-Supervised Activity Recognition Beyond Silent Daylight

Information Science, Electronic Information, Computer Science, Automation, Artificial Intelligence,
activity recognition   low-light conditions   audio-visual fusion   pseudo-supervised learning   darkness adaptation  

Research Highlights: Low-Light Activity Recognition Based on Pseudo-Supervision and Adaptive Audio-Visual Fusion Academic Context This paper investigates the challenges of recognizing activities under low-light conditions. While existing activity recognition technologies perform well in well-lit environments, they often fail when dealing with low-l...

EfficientDeRain+: Learning Uncertainty-Aware Filtering via RainMix Augmentation for High-Efficiency Deraining

Information Science, Electronic Information, Computer Science, Automation, Artificial Intelligence,
deraining   predictive filtering   cascaded dilated filtering   multi-scale filtering   data augmentation   RainMix  

EfficientDeRain+: A High-Efficiency Image Deraining Method Enhanced by RainMix Augmentation Background Rain significantly affects the quality of images and videos captured by computer vision systems, with raindrops and streaks impairing clarity and degrading performance in tasks like pedestrian detection, object tracking, and semantic segmentation....

Adaptive Middle Modality Alignment Learning for Visible-Infrared Person Re-Identification

Adaptive Middle Modality Alignment Learning for Visible-Infrared Person Re-Identification

Engineering, Electrical Engineering, Information Science, Electronic Information, Computer Science, Artificial Intelligence,
visible-infrared   person re-identification   middle modality   adaptive alignment   modality distribution   intelligent surveillance   modality discrepancy  

Research on Adaptive Middle-Modality Alignment Learning for Visible-Infrared Cross-Modality Learning Background and Problem Statement Driven by the needs of intelligent surveillance systems, visible-infrared person re-identification (VIReID) has gradually become a prominent research topic. This task aims to achieve around-the-clock person recogniti...