https://github.com/talalwasim/video-focalnets
공식 코드Video-FocalNets: Spatio-Temporal Focal Modulation for Video Action Recognition
Papers with Code 아카이브
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/tanghaoyu258/feel
공식 코드Visual Self-paced Iterative Learning for Unsupervised Temporal Action Localization
https://github.com/tda-jyamiti/d-gril
공식 코드D-GRIL: End-to-End Topological Learning with 2-parameter Persistence
https://github.com/tdavchev/structured-trajectory-prediction
공식 코드https://github.com/tecsar-uncc/adg-pose
공식 코드ADG-Pose: Automated Dataset Generation for Real-World Human Pose Estimation
https://github.com/tedhuang96/mifwlstm
공식 코드Long-term Pedestrian Trajectory Prediction using Mutable Intention Filter and Warp LSTM
https://github.com/tensorflow/models
공식 코드MoViNets: Mobile Video Networks for Efficient Video Recognition
https://github.com/thanhhff/HOTAD
공식 코드https://github.com/thartvigsen/grace
공식 코드Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors
https://github.com/thu-keg/event-level-knowledge-editing
공식 코드https://github.com/thunlp/eren
공식 코드https://github.com/tigerchen52/qrneuron
공식 코드Identifying Query-Relevant Neurons in Large Language Models for Long-Form Texts
https://github.com/tileb1/motion-prediction-tim
공식 코드https://github.com/tingz123/fha-kitchens
공식 코드https://github.com/tinyvision/IPCodec
공식 코드Spatiotemporal Entropy Model is All You Need for Learned Video Compression
https://github.com/tkipf/pygcn
공식 코드Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/tleemann/dpmhp
공식 코드Distribution Preserving Multiple Hypotheses Prediction for Uncertainty Modeling
https://github.com/tnbar/tednet
공식 코드Compressing Recurrent Neural Networks with Tensor Ring for Action Recognition
https://github.com/tonychenxyz/vit-interpret
공식 코드Interpreting and Controlling Vision Foundation Models via Text Explanations
https://github.com/tranxuantuyen/computer
공식 코드Unified Framework with Consistency across Modalities for Human Activity Recognition
https://github.com/trquhuytin/TOT-CVPR22
공식 코드Unsupervised Action Segmentation by Joint Representation Learning and Online Clustering
https://github.com/tsinghua-mars-lab/statetransformer
공식 코드Large Trajectory Models are Scalable Motion Predictors and Planners
https://github.com/ttgeng233/UniAV
공식 코드UniAV: Unified Audio-Visual Perception for Multi-Task Video Event Localization
https://github.com/ttlmh/bridge-prompt
공식 코드Bridge-Prompt: Towards Ordinal Action Understanding in Instructional Videos
https://github.com/ttlzfhy/D-DPCC
공식 코드D-DPCC: Deep Dynamic Point Cloud Compression via 3D Motion Prediction
https://github.com/tuantng/simon
공식 코드SimOn: A Simple Framework for Online Temporal Action Localization
https://github.com/tuantng/temporalmaxer
공식 코드TemporalMaxer: Maximize Temporal Context with only Max Pooling for Temporal Action Localization
https://github.com/tumftm/flexcloud
공식 코드FlexCloud: Direct, Modular Georeferencing and Drift-Correction of Point Cloud Maps
https://github.com/ty-97/seletive-volume-mix
공식 코드https://github.com/typistchen/spikmamba
공식 코드SpikMamba: When SNN meets Mamba in Event-based Human Action Recognition
https://github.com/uark-aicv/aoe-net
공식 코드https://github.com/uark-cviu/direcformer
공식 코드DirecFormer: A Directed Attention in Transformer Approach to Robust Action Recognition
https://github.com/ubisoftinc/Ubisoft-LaForge-Animation-Dataset
공식 코드https://github.com/uiuctml/TaskVectorBasis
공식 코드Efficient Model Editing with Task Vector Bases: A Theoretical Framework and Scalable Approach
https://github.com/una-dinosauria/human-motion-prediction
공식 코드https://github.com/uppaal/detox-edit
공식 코드Model Editing as a Robust and Denoised variant of DPO: A Case Study on Toxicity
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.