Papers with Code 아카이브
Activity Recognition
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- 4
- 보존된 평가 기록
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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/ruitian12/resformer
공식 코드https://github.com/ruiwang2021/mvd
공식 코드https://github.com/ruiyan1995/Interactive_Fusion_for_CAR
공식 코드Interactive Fusion of Multi-level Features for Compositional Activity Recognition
https://github.com/ruizehan/par
공식 코드https://github.com/ruizhuo-xu/skeleton2vec
공식 코드https://github.com/rupertluo/valley
공식 코드Valley: Video Assistant with Large Language model Enhanced abilitY
https://github.com/ruthless-man/awesome-learn-from-model
공식 코드https://github.com/rvl-lab-utoronto/video_similarity_search
공식 코드SLIC: Self-Supervised Learning with Iterative Clustering for Human Action Videos
https://github.com/rwightman/pytorch-image-models
공식 코드EVA: Exploring the Limits of Masked Visual Representation Learning at Scale
https://github.com/rymarinelli/leaking_lora
공식 코드Leaking LoRa: An Evaluation of Password Leaks and Knowledge Storage in Large Language Models
https://github.com/s-shamil/HandFormer
공식 코드https://github.com/sacrcv/activity-biometrics
공식 코드Activity-Biometrics: Person Identification from Daily Activities
https://github.com/sadafgulshad1/Hierarchical-ProtoPNet
공식 코드https://github.com/sadeghrahmanib/dear
공식 코드https://github.com/safwennaimi/representation-learning-for-skeleton-action-recognition-with-convolutional-transformers-and-byol
공식 코드https://github.com/sagarverma/EgoActionRecognition
공식 코드Making Third Person Techniques Recognize First-Person Actions in Egocentric Videos
https://github.com/saidwivedi/POCO
공식 코드https://github.com/saif-mahmud/hierarchical-attention-HAR
공식 코드Hierarchical Self Attention Based Autoencoder for Open-Set Human Activity Recognition
https://github.com/saif-mahmud/self-attention-HAR
공식 코드Human Activity Recognition from Wearable Sensor Data Using Self-Attention
https://github.com/sallymmx/actionclip
공식 코드https://github.com/samohtaerg/geoerm
공식 코드GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds
https://github.com/samyadeepbasu/locogen
공식 코드On Mechanistic Knowledge Localization in Text-to-Image Generative Models
https://github.com/samyak0210/ViNet
공식 코드ViNet: Pushing the limits of Visual Modality for Audio-Visual Saliency Prediction
https://github.com/sandeep-189/data-augmentation
공식 코드Transformer Networks for Data Augmentation of Human Physical Activity Recognition
https://github.com/sandy-zeng/pcl
공식 코드Towards Effective Image Manipulation Detection with Proposal Contrastive Learning
https://github.com/sangminwoo/actionmae
공식 코드Towards Good Practices for Missing Modality Robust Action Recognition
https://github.com/sankadivandya/ks-kss-dataset
공식 코드https://github.com/sanketsans/ganov2
공식 코드Enhancing Next Active Object-based Egocentric Action Anticipation with Guided Attention
https://github.com/sarinda251/cdfsl-v
공식 코드https://github.com/sayibet/fight-detection-surv-dataset
공식 코드https://github.com/sbargal/Caffe-ExcitationBP-RNNs
공식 코드https://github.com/scalable-model-editing/gradual-catastrophic-forgetting
공식 코드Model Editing at Scale leads to Gradual and Catastrophic Forgetting
https://github.com/scalable-model-editing/knowledge-editing-regularization
공식 코드https://github.com/scalable-model-editing/rebuilding-rome
공식 코드Rebuilding ROME : Resolving Model Collapse during Sequential Model Editing
https://github.com/scalable-model-editing/unified-model-editing
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.