https://github.com/triple7/Keras-WGAN-RGB-128x128
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Papers with Code 아카이브
Activity Recognition
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Human Activity Recognition is the problem of identifying events performed by humans given a video input. It is formulated as a binary (or multiclass) classification problem of outputting activity class labels. Activity Recognition is an important problem with many societal applications including smart surveillance, video search/retrieval, intelligent robots, and other monitoring systems.
Source: Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/tristangomez44/metrics-saliency-maps
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/tsenghungchen/SA-tensorflow
https://github.com/tsing-cv/EfficientNet-tensorflow-eager
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/ttengwang/coco-caption
https://github.com/tuananh0305/GCN_ANOMALY_DETECTION
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/tuetschek/e2e-metrics
https://github.com/turningpoint-ai/visualthinker-r1-zero
SAT: Dynamic Spatial Aptitude Training for Multimodal Language Models
https://github.com/txyugood/PaddleMVF
MVFNet: Multi-View Fusion Network for Efficient Video Recognition
https://github.com/txyugood/PaddlePoseC3D
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/usneek/sequence-embeddings
Activity2Vec: Learning ADL Embeddings from Sensor Data with a Sequence-to-Sequence Model
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/vohoaiviet/Advanced-Deep-Learning-with-Keras
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/wang-chen/correlation_flow
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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.