https://github.com/cybermonic/cage-4-submission
Semi-Supervised Classification with Graph Convolutional Networks
Papers with Code 아카이브
Activity Recognition
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- 평가표
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- 보존된 평가 기록
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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/da-head0/cctv-anomaly-detection
https://github.com/daheyinyin/CGAN
https://github.com/dais-ita/deepprobcep
https://github.com/dais-ita/vadr
https://github.com/damien911224/theWorldInSafety
Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/daniansan/i3d_mindspore
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/daniel-code/TubeViT
Rethinking Video ViTs: Sparse Video Tubes for Joint Image and Video Learning
https://github.com/danielchyeh/imagenet-100-pytorch
https://github.com/danielegrattarola/keras-gat
https://github.com/danielmatte/3D-ResNets-PyTorch-
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
https://github.com/danielpatrickhug/Research_Paper_Parser
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/daqingliu/coco-caption
https://github.com/darnbi/pygcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/darrylho/dejavid
https://github.com/darya-baranovskaya/keyword_spotting
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/davide-coccomini/TimeSformer-Video-Classification
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/davidpicard/homm
https://github.com/dc3ea9f/RegionViT
RegionViT: Regional-to-Local Attention for Vision Transformers
https://github.com/dddb11/MVSS-Net
Image Manipulation Detection by Multi-View Multi-Scale Supervision
https://github.com/debajyotid2/pointnet_pl
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/deepchem/moleculenet
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/deepmind/deepmind-research/tree/master/transporter
Unsupervised Learning of Object Keypoints for Perception and Control
https://github.com/deepmind/kinetics-i3d
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/denizyuret/playground
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/dhansmair/flamingo-mini
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/dineshssdn-867/Anomaly-Detection-CVPR2018
https://github.com/dineshzende/awesome-deep-learning-resources
https://github.com/dipika-singhania/multi-scale-action-banks
Temporal Aggregate Representations for Long-Range Video Understanding
https://github.com/dirtyharrylyl/hake-ava
HAKE: A Knowledge Engine Foundation for Human Activity Understanding
https://github.com/dlpbc/keras-kinetics-i3d
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/dmeoli/neuro-sat
https://github.com/dmis-lab/temporalhead
https://github.com/dmlc/dgl/tree/master/examples/mxnet/gat
https://github.com/dmlc/dgl/tree/master/examples/mxnet/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/dmlc/dgl/tree/master/examples/mxnet/sgc
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.