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

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

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

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

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

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...

Feature Matching via Graph Clustering with Local Affine Consensus

Feature Matching Based on Graph Clustering: Implementation and Application of Local Affine Consensus Academic Background and Motivation Feature matching is a fundamental problem in computer vision, playing a critical role in various tasks such as 3D reconstruction, image retrieval, image registration, and simultaneous localization and mapping (SLAM...

A Weakly Supervised Collaborative Procedure Alignment Framework for Procedural Video Analysis

Achieving Procedure-Aware Instructional Video Correlation Learning Under Weak Supervision: Summary and Evaluation In recent years, instructional videos have garnered significant attention due to their goal-driven characteristics and intrinsic connections to human learning processes. Compared to general videos, instructional videos contain multiple ...

Learning to Detect Novel Species with SAM in the Wild

Academic Paper Report: Open World Object Detection Framework Using SAM Background As the importance of ecosystem monitoring grows, the observation of wildlife and plant populations has become a crucial aspect of ecological conservation and agricultural development. These monitoring tasks include estimating population sizes, identifying species, stu...

MassiveFold: Unveiling AlphaFold’s Hidden Potential with Optimized and Parallelized Massive Sampling

Interpretation of “MassiveFold: Unveiling AlphaFold’s Hidden Potential with Optimized and Parallelized Massive Sampling” Background and Research Questions Protein structure prediction is a crucial area in life sciences, vital for understanding fundamental mechanisms in molecular biology. Recently, DeepMind’s AlphaFold achieved revolutionary progres...