https://github.com/WISION-Lab/eventful-transformer
공식 코드Eventful Transformers: Leveraging Temporal Redundancy in Vision Transformers
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/Walter0807/MotionBERT
공식 코드MotionBERT: A Unified Perspective on Learning Human Motion Representations
https://github.com/WenjieDu/PyPOTS
공식 코드TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
https://github.com/XZhang00/LU-LAFNs
공식 코드Multilingual Knowledge Editing with Language-Agnostic Factual Neurons
https://github.com/Xu-Linfeng/UESTC_MMEA_CL_main
공식 코드https://github.com/XudongLinthu/context-gated-convolution
공식 코드https://github.com/YangDi666/SSTA-PRS
공식 코드https://github.com/YangDi666/UNIK
공식 코드UNIK: A Unified Framework for Real-world Skeleton-based Action Recognition
https://github.com/YangLiu9208/SAKDN
공식 코드Semantics-aware Adaptive Knowledge Distillation for Sensor-to-Vision Action Recognition
https://github.com/YihengZhang-CV/SeCo-Sequence-Contrastive-Learning
공식 코드SeCo: Exploring Sequence Supervision for Unsupervised Representation Learning
https://github.com/YinxPeng/HA-CM-main
공식 코드https://github.com/Ymm-cll/UniErase
공식 코드UniErase: Unlearning Token as a Universal Erasure Primitive for Language Models
https://github.com/YorkUCVIL/Static-Dynamic-Interpretability
공식 코드https://github.com/Zedd1558/TwoStreamSepConvLSTM_ViolenceDetection
공식 코드Efficient Two-Stream Network for Violence Detection Using Separable Convolutional LSTM
https://github.com/ZeruiW/VideoXAI
공식 코드https://github.com/ZhaofanQiu/pseudo-3d-residual-networks
공식 코드Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks
https://github.com/ZhenyueQin/Angular-Skeleton-Encoding
공식 코드Fusing Higher-order Features in Graph Neural Networks for Skeleton-based Action Recognition
https://github.com/ZhouYuxuanYX/Hypergraph-Transformer-for-Skeleton-based-Action-Recognition
공식 코드Hypergraph Transformer for Skeleton-based Action Recognition
https://github.com/Zhow01/S2RKE
공식 코드https://github.com/ZichenMiao/GNN-with-Low-rank-Learnable-Local-Filters
공식 코드https://github.com/ZichenMiao/L3Net
공식 코드https://github.com/ZihaoZhao/SpikeGatingFlow
공식 코드https://github.com/ZikangLeng/IMUGPT
공식 코드https://github.com/ZiweiXU/CapsuleMotion
공식 코드Unsupervised Motion Representation Learning with Capsule Autoencoders
https://github.com/aaai-24/generative-based-kd
공식 코드Generative Model-based Feature Knowledge Distillation for Action Recognition
https://github.com/aashgan/temporalperturbsum
공식 코드Does SpatioTemporal information benefit Two video summarization benchmarks?
https://github.com/abcxyzi/radseg
공식 코드https://github.com/abcyzj/Semantics2Hands
공식 코드Semantics2Hands: Transferring Hand Motion Semantics between Avatars
https://github.com/abcyzj/meshret
공식 코드Skinned Motion Retargeting with Dense Geometric Interaction Perception
https://github.com/abduallahmohamed/har-gcnn
공식 코드HAR-GCNN: Deep Graph CNNs for Human Activity Recognition From Highly Unlabeled Mobile Sensor Data
https://github.com/abhinanda-punnakkal/BABEL
공식 코드https://github.com/acerkoo/disco
공식 코드Outdated Issue Aware Decoding for Reasoning Questions on Edited Knowledge
https://github.com/acewjh/fsfg
공식 코드Few-Shot Fine-Grained Action Recognition via Bidirectional Attention and Contrastive Meta-Learning
https://github.com/adam-bjtu/autocoa
공식 코드Agent models: Internalizing Chain-of-Action Generation into Reasoning models
https://github.com/adap/flower
공식 코드https://github.com/adaptivemotorcontrollab/llavaction
공식 코드LLaVAction: evaluating and training multi-modal large language models for action recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.