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Temporal Action Localization

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하위 태스크: 8
평가표: 14
보존된 평가 기록: 125

Temporal Action Localization aims to detect activities in the video stream and output beginning and end timestamps. It is closely related to Temporal Action Proposal Generation.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/davidpujol/SADA

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SADA: Semantic adversarial unsupervised domain adaptation for Temporal Action Localization

https://github.com/decisionforce/mmTransformer

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Multimodal Motion Prediction with Stacked Transformers

https://github.com/deepinmotion/autogcn

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AutoGCN -- Towards Generic Human Activity Recognition with Neural Architecture Search

https://github.com/deepintostreams/gcn-devlstm

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GCN-DevLSTM: Path Development for Skeleton-Based Action Recognition

https://github.com/deepmind/deepmind-research

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Unsupervised Learning of Object Keypoints for Perception and Control

https://github.com/deepmind/perception_test

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Perception Test: A Diagnostic Benchmark for Multimodal Video Models

https://github.com/derekpowell/taxi

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TAXI: Evaluating Categorical Knowledge Editing for Language Models

https://github.com/desehuileng0o0/ikem

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Elevating Skeleton-Based Action Recognition with Efficient Multi-Modality Self-Supervision

https://github.com/dingfengshi/tridet

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TriDet: Temporal Action Detection with Relative Boundary Modeling

https://github.com/dingfengshi/tridetplus

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Temporal Action Localization with Enhanced Instant Discriminability

https://github.com/dluvizon/deephar

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Multi-task Deep Learning for Real-Time 3D Human Pose Estimation and Action Recognition

https://github.com/dmytrozabolotnii/autoware_mini

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Pedestrian motion prediction evaluation for urban autonomous driving

https://github.com/domenicrosati/longform-evaluation-model-editing

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Long-form evaluation of model editing

https://github.com/dominickrei/poseawarevt

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Seeing the Pose in the Pixels: Learning Pose-Aware Representations in Vision Transformers

https://github.com/dong03/MVSS-Net

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Image Manipulation Detection by Multi-View Multi-Scale Supervision

https://github.com/droliven/diverse_sampling

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Diverse Human Motion Prediction via Gumbel-Softmax Sampling from an Auxiliary Space

https://github.com/dtamayo-nlp/memat

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Mass-Editing Memory with Attention in Transformers: A cross-lingual exploration of knowledge

https://github.com/dulucas/simlpe

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Back to MLP: A Simple Baseline for Human Motion Prediction

https://github.com/easonxiao-888/uvcom

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Bridging the Gap: A Unified Video Comprehension Framework for Moment Retrieval and Highlight Detection

https://github.com/ecnu-text-computing/knowledge-editing-benchmark

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Benchmarking and Rethinking Knowledge Editing for Large Language Models

https://github.com/edenbiran/rippleedits

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Evaluating the Ripple Effects of Knowledge Editing in Language Models

https://github.com/edgarschnfld/CADA-VAE-PyTorch

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Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders

https://github.com/edodema/BestPractices2Body

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Best Practices for 2-Body Pose Forecasting

https://github.com/edshieh/motionprediction

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A Mixture of Experts Approach to 3D Human Motion Prediction

https://github.com/edwardleelpz/powerbev

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PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird's-Eye View

https://github.com/ekgus9/COME

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CoME: An Unlearning-based Approach to Conflict-free Model Editing

https://github.com/emansim/unsupervised-videos

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Unsupervised Learning of Video Representations using LSTMs

https://github.com/eric-mitchell/mend

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Fast Model Editing at Scale

https://github.com/eric8932/jeep

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Joint Knowledge Editing for Information Enrichment and Probability Promotion

https://github.com/eth-ait/motion-transformer

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A Spatio-temporal Transformer for 3D Human Motion Prediction

https://github.com/evendrow/somoformer

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SoMoFormer: Multi-Person Pose Forecasting with Transformers

https://github.com/event-ahu/celex-har

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Event Stream based Human Action Recognition: A High-Definition Benchmark Dataset and Algorithms

https://github.com/event-ahu/hardvs

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HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors

https://github.com/fabienbaradel/glimpse_clouds

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Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points

https://github.com/facebookresearch/GloRe

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Graph-Based Global Reasoning Networks

https://github.com/facebookresearch/Motionformer

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Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers

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