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Action Localization
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Action Localization is finding the spatial and temporal co ordinates for an action in a video. An action localization model will identify which frame an action start and ends in video and return the x,y coordinates of an action. Further the co ordinates will change when the object performing action undergoes a displacement.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/hi-archers/mlake
공식 코드MLaKE: Multilingual Knowledge Editing Benchmark for Large Language Models
https://github.com/hikvision-research/cost
공식 코드Collaborative Spatio-temporal Feature Learning for Video Action Recognition
https://github.com/hikvision-research/skelact
공식 코드Skeleton-based Action Recognition with Convolutional Neural Networks
https://github.com/hitcbw/lpl
공식 코드Language-Assisted Human Part Motion Learning for Skeleton-Based Temporal Action Segmentation
https://github.com/hiyouga/llama-factory
공식 코드Time Sensitive Knowledge Editing through Efficient Finetuning
https://github.com/hkust-aerial-robotics/dsp
공식 코드Trajectory Prediction with Graph-based Dual-scale Context Fusion
https://github.com/hkust-aerial-robotics/simpl
공식 코드SIMPL: A Simple and Efficient Multi-agent Motion Prediction Baseline for Autonomous Driving
https://github.com/hlcdyy/pan-motion-retargeting
공식 코드Pose-aware Attention Network for Flexible Motion Retargeting by Body Part
https://github.com/hoagyc/sparse_coding
공식 코드Sparse Autoencoders Find Highly Interpretable Features in Language Models
https://github.com/holi-lab/core
공식 코드https://github.com/holistic-video-understanding/HVU-Dataset
공식 코드https://github.com/holzbock/st_mlp
공식 코드A Spatio-Temporal Multilayer Perceptron for Gesture Recognition
https://github.com/hongsong-wang/PVRNN
공식 코드PVRED: A Position-Velocity Recurrent Encoder-Decoder for Human Motion Prediction
https://github.com/hongyan1123/skeletonmae
공식 코드SkeletonMAE: Graph-based Masked Autoencoder for Skeleton Sequence Pre-training
https://github.com/hotfinda/videomambapro
공식 코드https://github.com/houshd/MLSTM-FCN
공식 코드https://github.com/howardzh01/ppma
공식 코드Learning Human Action Recognition Representations Without Real Humans
https://github.com/huangxiusheng/dem
공식 코드https://github.com/huiguanlab/hico
공식 코드Hierarchical Contrast for Unsupervised Skeleton-based Action Representation Learning
https://github.com/hulianyuyy/STGAT
공식 코드Spatial Temporal Graph Attention Network for Skeleton-Based Action Recognition
https://github.com/hustyyq/where-to-edit
공식 코드https://github.com/hvision-nku/storydiffusion
공식 코드StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation
https://github.com/hwj20/apex_exp
공식 코드APEX: Empowering LLMs with Physics-Based Task Planning for Real-time Insight
https://github.com/hypjudy/Decouple-SSAD
공식 코드Decoupling Localization and Classification in Single Shot Temporal Action Detection
https://github.com/hyqlat/ATACB
공식 코드Stochastic Human Motion Prediction with Memory of Action Transition and Action Characteristic
https://github.com/hyqlat/tcl
공식 코드Temporal Continual Learning with Prior Compensation for Human Motion Prediction
https://github.com/hyunjs/sgt
공식 코드Detection Recovery in Online Multi-Object Tracking with Sparse Graph Tracker
https://github.com/hyunjs/stov-tal
공식 코드Exploring Scalability of Self-Training for Open-Vocabulary Temporal Action Localization
https://github.com/iai-hrc/ha-vid
공식 코드HA-ViD: A Human Assembly Video Dataset for Comprehensive Assembly Knowledge Understanding
https://github.com/icidsl/harflow3d
공식 코드HARFLOW3D: A Latency-Oriented 3D-CNN Accelerator Toolflow for HAR on FPGA Devices
https://github.com/ideal-idea/sap
공식 코드https://github.com/idiap/potr
공식 코드Pose Transformers (POTR): Human Motion Prediction with Non-Autoregressive Transformers
https://github.com/idruglab/adcnet
공식 코드ADCNet: a unified framework for predicting the activity of antibody-drug conjugates
https://github.com/idt-iti/mmfusion-iml
공식 코드MMFusion: Combining Image Forensic Filters for Visual Manipulation Detection and Localization
https://github.com/iduta/iresnet
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.