https://bitbucket.org/sahasuman/bmvc2016_code
공식 코드Online Spatiotemporal Action Detection and Prediction via Causal Representations
Papers with Code 아카이브
Action Understanding
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AIM3-RUC/YouMakeup_Baseline
공식 코드YouMakeup VQA Challenge: Towards Fine-grained Action Understanding in Domain-Specific Videos
https://github.com/ASU-APG/TragedyPlusTime
공식 코드Tragedy Plus Time: Capturing Unintended Human Activities from Weakly-labeled Videos
https://github.com/Buzz-Beater/LEMMA
공식 코드LEMMA: A Multi-view Dataset for Learning Multi-agent Multi-task Activities
https://github.com/DirtyHarryLYL/DJ-RN
공식 코드Detailed 2D-3D Joint Representation for Human-Object Interaction
https://github.com/ParitoshParmar/Fitness-AQA
공식 코드Domain Knowledge-Informed Self-Supervised Representations for Workout Form Assessment
https://github.com/ParitoshParmar/Win-Fail-Action-Recognition
공식 코드https://github.com/acewjh/fsfg
공식 코드Few-Shot Fine-Grained Action Recognition via Bidirectional Attention and Contrastive Meta-Learning
https://github.com/adaptivemotorcontrollab/llavaction
공식 코드LLaVAction: evaluating and training multi-modal large language models for action recognition
https://github.com/echo0125/memory-and-anticipation-transformer
공식 코드Memory-and-Anticipation Transformer for Online Action Understanding
https://github.com/f3set/f3set
공식 코드F$^3$Set: Towards Analyzing Fast, Frequent, and Fine-grained Events from Videos
https://github.com/hoplee6/sports-qa
공식 코드Sports-QA: A Large-Scale Video Question Answering Benchmark for Complex and Professional Sports
https://github.com/huiguanlab/umurl
공식 코드Unified Multi-modal Unsupervised Representation Learning for Skeleton-based Action Understanding
https://github.com/hutch-matt/vau-tutorial
공식 코드https://github.com/iriya99/ovre
공식 코드https://github.com/isee-laboratory/egoexo-fitness
공식 코드EgoExo-Fitness: Towards Egocentric and Exocentric Full-Body Action Understanding
https://github.com/jhang2020/pcm3
공식 코드Self-Supervised Skeleton-Based Action Representation Learning: A Benchmark and Beyond
https://github.com/jhong93/vpd
공식 코드Video Pose Distillation for Few-Shot, Fine-Grained Sports Action Recognition
https://github.com/kylehuang9/sefar
공식 코드https://github.com/likenneth/acton
공식 코드https://github.com/lizhi1104/haan
공식 코드Weakly-Supervised Temporal Action Detection for Fine-Grained Videos with Hierarchical Atomic Actions
https://github.com/mikewangwzhl/paxion
공식 코드Paxion: Patching Action Knowledge in Video-Language Foundation Models
https://github.com/minghu0830/ophnet-benchmark
공식 코드OphNet: A Large-Scale Video Benchmark for Ophthalmic Surgical Workflow Understanding
https://github.com/nishantrai18/homage
공식 코드Home Action Genome: Cooperative Compositional Action Understanding
https://github.com/ody-trek/llava-pose
공식 코드LLaVA-Pose: Enhancing Human Pose and Action Understanding via Keypoint-Integrated Instruction Tuning
https://github.com/pku-icst-mipl/fineparser_cvpr2024
공식 코드FineParser: A Fine-grained Spatio-temporal Action Parser for Human-centric Action Quality Assessment
https://github.com/redwang/DTGRM
공식 코드Temporal Relational Modeling with Self-Supervision for Action Segmentation
https://github.com/reyuwei/PIANO_model
공식 코드PIANO: A Parametric Hand Bone Model from Magnetic Resonance Imaging
https://github.com/ttlmh/bridge-prompt
공식 코드Bridge-Prompt: Towards Ordinal Action Understanding in Instructional Videos
https://github.com/JHang2020/Shap-Mix
Prompted Contrast with Masked Motion Modeling: Towards Versatile 3D Action Representation Learning
https://github.com/baiyang4/aqa_tpt
https://github.com/damien911224/theWorldInSafety
Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.