https://github.com/tobyperrett/trx
공식 코드Temporal-Relational CrossTransformers for Few-Shot Action Recognition
Papers with Code 아카이브
Activity Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 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/tonychenxyz/vit-interpret
공식 코드Interpreting and Controlling Vision Foundation Models via Text Explanations
https://github.com/tonysy/PyAction
공식 코드Learning Implicit Temporal Alignment for Few-shot Video Classification
https://github.com/toytiny/milliflow
공식 코드milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing
https://github.com/tranxuantuyen/computer
공식 코드Unified Framework with Consistency across Modalities for Human Activity Recognition
https://github.com/trungmanhhuynh/GPRAR
공식 코드https://github.com/tszheichoi/awesome-sensor-logger
공식 코드ALS-HAR: Harnessing Wearable Ambient Light Sensors to Enhance IMU-based Human Activity Recogntion
https://github.com/ttlmh/bridge-prompt
공식 코드Bridge-Prompt: Towards Ordinal Action Understanding in Instructional Videos
https://github.com/tumeteor/neurips2019challenge
공식 코드Spatiotemporal Tile-based Attention-guided LSTMs for Traffic Video Prediction
https://github.com/twelvelabs-io/video-embeddings-evaluation-framework
공식 코드TWLV-I: Analysis and Insights from Holistic Evaluation on Video Foundation Models
https://github.com/twyncoder/vidimu-tools
공식 코드https://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/video_representation
공식 코드Contextual Explainable Video Representation: Human Perception-based Understanding
https://github.com/uark-cviu/direcformer
공식 코드DirecFormer: A Directed Attention in Transformer Approach to Robust Action Recognition
https://github.com/uark-cviu/spartan
공식 코드SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition
https://github.com/ucla-mobility/AutoVLA
공식 코드https://github.com/uiuctml/TaskVectorBasis
공식 코드Efficient Model Editing with Task Vector Bases: A Theoretical Framework and Scalable Approach
https://github.com/uppaal/detox-edit
공식 코드Model Editing as a Robust and Denoised variant of DPO: A Case Study on Toxicity
https://github.com/utomm/mini-diffuse-actor
공식 코드Mini Diffuser: Fast Multi-task Diffusion Policy Training Using Two-level Mini-batches
https://github.com/uva-dsa/ems-pipeline
공식 코드Real-Time Multimodal Cognitive Assistant for Emergency Medical Services
https://github.com/uwnlp/verb-attributes
공식 코드Zero-Shot Activity Recognition with Verb Attribute Induction
https://github.com/vaidehi99/infodeletionattacks
공식 코드Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks
https://github.com/valterlej/objsentzsar
공식 코드Global Semantic Descriptors for Zero-Shot Action Recognition
https://github.com/valterlej/zsarcap
공식 코드Tell me what you see: A zero-shot action recognition method based on natural language descriptions
https://github.com/vedb/vedb_gaze_1.0
공식 코드https://github.com/vicentealex/dvs-gesture-chain
공식 코드Spiking Neural Networks for event-based action recognition: A new task to understand their advantage
https://github.com/vicky-wil/remake
공식 코드https://github.com/villawang/team-net
공식 코드TEAM-Net: Multi-modal Learning for Video Action Recognition with Partial Decoding
https://github.com/vishal3477/proactive_imd
공식 코드https://github.com/visurg-ai/surg-3m
공식 코드Surg-3M: A Dataset and Foundation Model for Perception in Surgical Settings
https://github.com/vita-epfl/bounding-box-prediction
공식 코드https://github.com/vivoutlaw/tcbp
공식 코드https://github.com/vlkeb/vlkeb
공식 코드VLKEB: A Large Vision-Language Model Knowledge Editing Benchmark
https://github.com/void-96/har_tencon2022
공식 코드Handwashing Action Detection System for an Autonomous Social Robot
https://github.com/vt-vl-lab/SDN
공식 코드Why Can't I Dance in the Mall? Learning to Mitigate Scene Bias in Action Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.