https://github.com/mchengny/RWF2000-Video-Database-for-Violence-Detection
공식 코드RWF-2000: An Open Large Scale Video Database for Violence Detection
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Action Recognition is a computer vision task that involves recognizing human actions in videos or images. The goal is to classify and categorize the actions being performed in the video or image into a predefined set of action classes.
In the video domain, it is an open question whether training an action classification network on a sufficiently large dataset, will give a similar boost in performance when applied to a different temporal task or dataset. The challenges of building video datasets has meant that most popular benchmarks for action recognition are small, having on the order of 10k videos.
Please note some benchmarks may be located in the Action Classification or Video Classification tasks, e.g. Kinetics-400.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
RWF-2000: An Open Large Scale Video Database for Violence Detection
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
Fusion-GCN: Multimodal Action Recognition using Graph Convolutional Networks
Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional Connectivity
Egocentric Action-aware Inertial Localization in Point Clouds
Two-person Graph Convolutional Network for Skeleton-based Human Interaction Recognition
Building a Multi-modal Spatiotemporal Expert for Zero-shot Action Recognition with CLIP
Unsupervised Visual Representation Learning by Tracking Patches in Video
Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition
Expanding Language-Image Pretrained Models for General Video Recognition
A System for Real-Time Interactive Analysis of Deep Learning Training
Expanding Language-Image Pretrained Models for General Video Recognition
Concatenated Masked Autoencoders as Spatial-Temporal Learner
From Forest to Zoo: Great Ape Behavior Recognition with ChimpBehave
Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization
Untying the Reversal Curse via Bidirectional Language Model Editing
Neighboring Perturbations of Knowledge Editing on Large Language Models
IntegralAction: Pose-driven Feature Integration for Robust Human Action Recognition in Videos
vid-TLDR: Training Free Token merging for Light-weight Video Transformer
Actor-agnostic Multi-label Action Recognition with Multi-modal Query
A Dynamic Spatial-temporal Attention Network for Early Anticipation of Traffic Accidents
MOFO: MOtion FOcused Self-Supervision for Video Understanding
Improving Skeleton-based Action Recognition with Interactive Object Information
Multi-attention Networks for Temporal Localization of Video-level Labels
EventRPG: Event Data Augmentation with Relevance Propagation Guidance
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.