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/pseulki/rococo
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/pwc-1/Paper-10/tree/main/timesformer
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/pwc-1/Paper-8/tree/main/clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/pwc-1/Paper-9/tree/main/1/timesformer
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/pwc-1/Paper-9/tree/main/1/vivit
https://github.com/pwc-1/Paper-9/tree/main/3/AVA_cifar
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
https://github.com/pwc-1/Paper-9/tree/main/3/vat
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
https://github.com/pwc-1/Paper-9/tree/main/4/TSA_mindspore
TSA-Net: Tube Self-Attention Network for Action Quality Assessment
https://github.com/pwc-1/Paper-9/tree/main/5/videomae
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
https://github.com/pytorch/vision
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/qema/orca-py
https://github.com/qijiezhao/pseudo-3d-pytorch
Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks
https://github.com/qingzwang/DiversityMetrics
https://github.com/qubvel/efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/qwarts/video_classification
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
https://github.com/qwopqwop200/SoftPool
https://github.com/rajatmodi62/occludedactionbenchmark
MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
https://github.com/rajneeshaggarwal/google-efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/rajskar/CS763Project
RED: Reinforced Encoder-Decoder Networks for Action Anticipation
https://github.com/rakhimovv/lvt
https://github.com/ramanakshay/clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/raphaelmemmesheimer/gimme_signals_action_recognition
Gimme Signals: Discriminative signal encoding for multimodal activity recognition
https://github.com/ravi02512/efficientdet-keras
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/raymondyeh07/chirality_nets
https://github.com/reallm-labs/infiguiagent
InfiGUI-R1: Advancing Multimodal GUI Agents from Reactive Actors to Deliberative Reasoners
https://github.com/redcaps-dataset/redcaps-downloader
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/rejuvyesh/rllab
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/rekon/T3D-keras
Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification
https://github.com/reyvaz/pneumothorax_detection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/reyvaz/steel-defect-segmentation
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/rhgao/Deep-MIML-Network
Im2Flow: Motion Hallucination from Static Images for Action Recognition
https://github.com/rhgao/Im2Flow
Im2Flow: Motion Hallucination from Static Images for Action Recognition
https://github.com/rhgao/separating-object-sounds
Im2Flow: Motion Hallucination from Static Images for Action Recognition
https://github.com/richardrl/cartpole-request-for-research
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/rickyHong/tfoptflow-repl
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.