https://github.com/jachiam/cpo
Benchmarking Deep Reinforcement Learning for Continuous Control
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/jacksonchen1998/Cold-Start-Reinforcement-Learning-with-Softmax-Policy-Gradient
https://github.com/jacksonkaunismaa/neural-music
https://github.com/jacobgil/pytorch-grad-cam
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/jacobkimmel/pytorch_convgru
Delving Deeper into Convolutional Networks for Learning Video Representations
https://github.com/jaketae/mlp-mixer
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/james77777778/keras-image-models
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/jamesloyys/PyTorch-Lightning-GAN
https://github.com/jason90330/EdgeFinal
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/jayChung0302/videomix
VideoMix: Rethinking Data Augmentation for Video Classification
https://github.com/jayleicn/ClipBERT
https://github.com/jbloomaus/saelens
Sparse Autoencoders Find Highly Interpretable Features in Language Models
https://github.com/jeffreyhuang1/two-stream-action-recognition
TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition
https://github.com/jeffreyyihuang/two-stream-action-recognition
TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition
https://github.com/jerryljq/ActionRecognition
Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/jerrywn121/TianChi_AIEarth
Convolutional Tensor-Train LSTM for Spatio-temporal Learning
https://github.com/jettdlee/vis_tac_cross_modal
https://github.com/jhaprince/multibully
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/jhchang/DFDC
https://github.com/jiajunhua/facebookresearch-Detectron
https://github.com/jiangboahu/glcn-tf
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/jihun-kr/SlowFast
https://github.com/jindongwang/deep-learning-activity-recognition
Deep Learning for Sensor-based Activity Recognition: A Survey
https://github.com/jmhessel/pycocoevalcap
https://github.com/joaanna/something_else
Something-Else: Compositional Action Recognition with Spatial-Temporal Interaction Networks
https://github.com/joaopedromattos/gnee
https://github.com/jonmun/EPIC-KITCHENS-100_UDA_TA3N
https://github.com/jonmun/MM-SADA-code
Multi-Modal Domain Adaptation for Fine-Grained Action Recognition
https://github.com/jordiae/DeepLearning-MAI
https://github.com/jpeyre/unrel
Learning from Video and Text via Large-Scale Discriminative Clustering
https://github.com/jpviguerasguillen/feedback-non-local-attention-fnla
https://github.com/jpviguerasguillen/feedback-non-local-attention_fNLA
https://github.com/js-aguiar/wheat-object-detection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/jtpils/treegan
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/juenkhaw/action_recognition_project
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/juenkhaw/action_recognition_project1
A Closer Look at Spatiotemporal Convolutions for Action Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.