arXiv:2408.14659
공식 코드 · Papers with Code
Comparative Analysis: Violence Recognition from Videos using Transfer Learning
원문 초록
Action recognition has become a hot topic in computer vision. However, the main applications of computer vision in video processing have focused on detection of relatively simple actions while complex events such as violence detection have been comparatively less investigated. This study focuses on the benchmarking of various deep learning techniques on a complex dataset. Next, a larger dataset is utilized to test the uplift from increasing volume of data. The dataset size increase from 500 to 1,600 videos resulted in a notable average accuracy improvement of 6% across four models.
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Zero-shot Skeleton-based Action Recognition with Prototype-guided Feature Alignment
Benchmarking Deep Learning and Vision Foundation Models for Atypical vs. Normal Mitosis Classification with Cross-Dataset Evaluation
Benchmarking histopathology foundation models in a multi-center dataset for skin cancer subtyping
HopaDIFF: Holistic-Partial Aware Fourier Conditioned Diffusion for Referring Human Action Segmentation in Multi-Person Scenarios
Disentangling coincident cell events using deep transfer learning and compressive sensing
Calibrated and Robust Foundation Models for Vision-Language and Medical Image Tasks Under Distribution Shift
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