Action Recognition
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/tylin/coco-caption
https://github.com/udaydutt12/Neural-Nets
https://github.com/udlf/wsef
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/ultralytics/yolov5
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/unispac/visual-adversarial-examples-jailbreak-large-language-models
https://github.com/urw7rs/torch_skeleton
Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition
https://github.com/v1an1/gradcamplusplus-cats-v-dogs
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/vailatuts/SSFG-regularization
https://github.com/vanoracai/Exploiting-Spatial-temporal-Relationships-for-3D-Pose-Estimation-via-Graph-Convolutional-Networks
https://github.com/vcowwy/paddle-grad-cam
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/vijayvee/behavior-recognition
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/vijayvee/behavior_recognition
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/vinits5/learning3d
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/visaVita/mmaction2
https://github.com/vladthesav/MoldAI
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/vlomonaco/US-TransportationMode
Custom Dual Transportation Mode Detection by Smartphone Devices Exploiting Sensor Diversity
https://github.com/vrama91/cider
https://github.com/vrama91/coco-caption
https://github.com/w-sugar/Video-Swin-Transformer
https://github.com/wan2000/mc-mot
https://github.com/wangjk666/stts
https://github.com/wanglichenxj/EV-Action-Electromyography-Vision-Multi-Modal-Action-Dataset
EV-Action: Electromyography-Vision Multi-Modal Action Dataset
https://github.com/wangxiang1230/SSTAP
https://github.com/wangyi111/international-archaeology-ai-challenge
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/wasilone11/ICPR-RIP-2024
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/waynshang/Gesture-Recognition-with-3DCNN
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/weixuan-wang123/ReMaKE
Editing Large Language Models: Problems, Methods, and Opportunities
https://github.com/weiyangfb/PyTorchSparseGAT
https://github.com/wenhuchen/data-to-text-evaluation-metric
https://github.com/whut2962575697/gat_sementic_segmentation
https://github.com/whwu95/Cap4Video
Revisiting Classifier: Transferring Vision-Language Models for Video Recognition
https://github.com/whwu95/GPT4Vis
Revisiting Classifier: Transferring Vision-Language Models for Video Recognition
https://github.com/witignite/Frustum-PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/wonderland-dsg/pointnet-grid
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/woodfrog/ActionRecognition
Two-Stream Convolutional Networks for Action Recognition in Videos
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.