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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/mgciszewski/improving_state_estimation_2022

공식 코드

Improving state estimation through projection post-processing for activity recognition with application to football

https://github.com/mgiant/2p-gcn

공식 코드

Two-person Graph Convolutional Network for Skeleton-based Human Interaction Recognition

https://github.com/mgiant/mp-gcn

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Skeleton-based Group Activity Recognition via Spatial-Temporal Panoramic Graph

https://github.com/mia-yatingyu/stdd

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Building a Multi-modal Spatiotemporal Expert for Zero-shot Action Recognition with CLIP

https://github.com/microsoft/CtP

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Unsupervised Visual Representation Learning by Tracking Patches in Video

https://github.com/microsoft/SGN

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Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition

https://github.com/microsoft/UFO

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Large Action Models: From Inception to Implementation

https://github.com/microsoft/VideoX/tree/master/X-CLIP

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Expanding Language-Image Pretrained Models for General Video Recognition

https://github.com/microsoft/View-Adaptive-Neural-Networks-for-Skeleton-based-Human-Action-Recognition

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View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data

https://github.com/microsoft/robustlearn

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Out-of-Distribution Representation Learning for Time Series Classification

https://github.com/microsoft/tensorwatch

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A System for Real-Time Interactive Analysis of Deep Learning Training

https://github.com/microsoft/videox

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Expanding Language-Image Pretrained Models for General Video Recognition

https://github.com/milandesai/yolov3

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Spatiotemporal Action Recognition in Restaurant Videos

https://github.com/mims-harvard/tfc-pretraining

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Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency

https://github.com/minhoooo1/catmae

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Concatenated Masked Autoencoders as Spatial-Temporal Learner

https://github.com/mitchfuchs/chimpbehave

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From Forest to Zoo: Great Ape Behavior Recognition with ChimpBehave

https://github.com/miulab/editllm-survey

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Editing the Mind of Giants: An In-Depth Exploration of Pitfalls of Knowledge Editing in Large Language Models

https://github.com/mjkwon2021/cat-net

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Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization

https://github.com/mjy1111/bake

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Untying the Reversal Curse via Bidirectional Language Model Editing

https://github.com/mjy1111/peak

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Neighboring Perturbations of Knowledge Editing on Large Language Models

https://github.com/mjy1111/prune

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Perturbation-Restrained Sequential Model Editing

https://github.com/mks0601/IntegralAction_RELEASE

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IntegralAction: Pose-driven Feature Integration for Robust Human Action Recognition in Videos

https://github.com/ml-postech/skeleton-anonymization

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Anonymization for Skeleton Action Recognition

https://github.com/mlgig/amee

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Robust Explainer Recommendation for Time Series Classification

https://github.com/mlvccn/bmtc_transferattackvid

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Temporal Consistency Constrained Transferable Adversarial Attacks with Background Mixup for Action Recognition

https://github.com/mlvlab/vid-tldr

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vid-TLDR: Training Free Token merging for Light-weight Video Transformer

https://github.com/mmalekzadeh/dana

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DANA: Dimension-Adaptive Neural Architecture for Multivariate Sensor Data

https://github.com/mmalekzadeh/motion-sense

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Protecting Sensory Data against Sensitive Inferences

https://github.com/mmalekzadeh/replacement-autoencoder

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Replacement AutoEncoder: A Privacy-Preserving Algorithm for Sensory Data Analysis

https://github.com/mobilellm/llm-explorer

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LLM-Explorer: Towards Efficient and Affordable LLM-based Exploration for Mobile Apps

https://github.com/modelscope/data-juicer

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HumanVBench: Exploring Human-Centric Video Understanding Capabilities of MLLMs with Synthetic Benchmark Data

https://github.com/momilijaz96/mmt_for_ncrc

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Multimodal Transformer for Nursing Activity Recognition

https://github.com/mondalanindya/msqnet

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Actor-agnostic Multi-label Action Recognition with Multi-modal Query

https://github.com/monjurulkarim/DSTA

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A Dynamic Spatial-temporal Attention Network for Early Anticipation of Traffic Accidents

https://github.com/moohnai/mofo

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MOFO: MOtion FOcused Self-Supervision for Video Understanding

https://github.com/moonlight52137/st-vgcn

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Improving Skeleton-based Action Recognition with Interactive Object Information

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.