Papers with Code 아카이브
Activity Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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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/taufikxu/youtube
https://github.com/tea1528/Non-Local-NN-Pytorch
https://github.com/technion-cs-nlp/llmsknow
Editing Implicit Assumptions in Text-to-Image Diffusion Models
https://github.com/tengdahan/temporalalignnet
https://github.com/thaitruongan/camera-surveillance-ai
https://github.com/thakurnupur/Sparse-Adversarial-Perturbations-PyTorch
https://github.com/thanhtrunghuynh93/pygcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/thefonseca/predictive-coding
Moments in Time Dataset: one million videos for event understanding
https://github.com/thestephencasper/latent_adversarial_training
Latent Adversarial Training Improves Robustness to Persistent Harmful Behaviors in LLMs
https://github.com/thuml/Time-Series-Library
TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
https://github.com/thuml/timesnet
TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
https://github.com/tianfr/mononerf
https://github.com/tianfr/semantic-flow
https://github.com/tianhai123/3D-ResNets
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
https://github.com/timeseriesAI/tsai/blob/main/tsai/models/RNN_FCN.py
https://github.com/timothylimyl/PointNet-Pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/tinymilky/RSANet
https://github.com/titu1994/LSTM-FCN
https://github.com/titu1994/MLSTM-FCN
https://github.com/titu1994/keras-efficientnets
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/titu1994/tf_star_rnn
https://github.com/tk-rusch/graphcon
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/tkipf/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/tkipf/keras-gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/tlmakinen/GAT-noise
https://github.com/toanbkmt/EfficientnetFruitDetect
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/tobyperrett/few-shot-action-recognition
Temporal-Relational CrossTransformers for Few-Shot Action Recognition
https://github.com/toinesayan/node-classification-and-label-dependencies
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/tomar840/two-stream-fusion-for-action-recognition-in-videos
Convolutional Two-Stream Network Fusion for Video Action Recognition
https://github.com/totalgood/viddesc
https://github.com/totti0223/gradcamplusplus
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/towhee-io/towhee
Actor-Context-Actor Relation Network for Spatio-Temporal Action Localization
https://github.com/towhee-io/towhee/tree/main/towhee/models/tsm
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/toytiny/radarocc
milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing
https://github.com/trevor-richardson/rnn_zoo
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.