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Activity Recognition

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하위 태스크: 11
평가표: 4
보존된 평가 기록: 10

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/xuyu0010/awesome-video-domain-adaptation

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Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Survey

https://github.com/xyanchen/wifi-csi-sensing-benchmark

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AutoFi: Towards Automatic WiFi Human Sensing via Geometric Self-Supervised Learning

https://github.com/xyzforever/bevt

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BEVT: BERT Pretraining of Video Transformers

https://github.com/y-max/TransFall

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Transfer Learning for Activity Recognition in Mobile Health

https://github.com/yanbeic/CCL

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Distilling Audio-Visual Knowledge by Compositional Contrastive Learning

https://github.com/yangliu9208/tcgl

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TCGL: Temporal Contrastive Graph for Self-supervised Video Representation Learning

https://github.com/yangzhenkui/witalk

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Talk is Not Always Cheap: Promoting Wireless Sensing Models with Text Prompts

https://github.com/yanhui002/video_adv

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Sparse Adversarial Perturbations for Videos

https://github.com/yaohui120/comprehendedit

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ComprehendEdit: A Comprehensive Dataset and Evaluation Framework for Multimodal Knowledge Editing

https://github.com/yaoli/arctic-capgen-vid

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Describing Videos by Exploiting Temporal Structure

https://github.com/ybdai7/NAMET-massive-editing

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NAMET: Robust Massive Model Editing via Noise-Aware Memory Optimization

https://github.com/ybhbingo/mmfi_dataset

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MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Versatile Wireless Sensing

https://github.com/yellowtownhz/STIGCN

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Spatio-Temporal Inception Graph Convolutional Networks for Skeleton-Based Action Recognition

https://github.com/yfsong0709/EfficientGCNv1

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Constructing Stronger and Faster Baselines for Skeleton-based Action Recognition

https://github.com/yfsong0709/RA-GCNv2

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Richly Activated Graph Convolutional Network for Robust Skeleton-based Action Recognition

https://github.com/yhZhai/SOAR

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SOAR: Scene-debiasing Open-set Action Recognition

https://github.com/yhZhai/WSCL

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Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learning

https://github.com/yijieyang23/skelet-gcn

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Expressive Keypoints for Skeleton-based Action Recognition via Skeleton Transformation

https://github.com/yilliajing/action-ood

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Skeleton-OOD: An End-to-End Skeleton-Based Model for Robust Out-of-Distribution Human Action Detection

https://github.com/yilliajing/skeleton-ood

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Skeleton-OOD: An End-to-End Skeleton-Based Model for Robust Out-of-Distribution Human Action Detection

https://github.com/yinmh17/DNL-Semantic-Segmentation

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Disentangled Non-Local Neural Networks

https://github.com/yjiangcm/lte

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Learning to Edit: Aligning LLMs with Knowledge Editing

https://github.com/yjxiong/caffe

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Towards Good Practices for Very Deep Two-Stream ConvNets

https://github.com/yjxiong/temporal-segment-networks

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Temporal Segment Networks: Towards Good Practices for Deep Action Recognition

https://github.com/yjyddq/CLAVER

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Kronecker Mask and Interpretive Prompts are Language-Action Video Learners

https://github.com/yoavarad/mfad

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Beyond the Benchmark: Detecting Diverse Anomalies in Videos

https://github.com/youngwanLEE/VoV3D

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Diverse Temporal Aggregation and Depthwise Spatiotemporal Factorization for Efficient Video Classification

https://github.com/youshyee/CEP

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Back to the Future: Cycle Encoding Prediction for Self-supervised Contrastive Video Representation Learning

https://github.com/yuanyao366/PRP

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Video Playback Rate Perception for Self-supervisedSpatio-Temporal Representation Learning

https://github.com/yucornetto/CAKES

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CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D Networks

https://github.com/yujieouo/pstl

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Self-supervised Action Representation Learning from Partial Spatio-Temporal Skeleton Sequences

https://github.com/yujieouo/smie

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Zero-shot Skeleton-based Action Recognition via Mutual Information Estimation and Maximization

https://github.com/yujmo/czu_mhad

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CZU-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and 10 wearable inertial sensors

https://github.com/yunfengdiao/Skeleton-Robustness-Benchmark

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TASAR: Transfer-based Attack on Skeletal Action Recognition

https://github.com/yuxiaochen1103/DG-STA

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Construct Dynamic Graphs for Hand Gesture Recognition via Spatial-Temporal Attention

https://github.com/yuxiaochen1103/Hi-TRS

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Hierarchically Self-Supervised Transformer for Human Skeleton Representation Learning

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