https://bitbucket.org/sahasuman/bmvc2016_code
공식 코드Online Spatiotemporal Action Detection and Prediction via Causal Representations
Papers with Code 아카이브
Temporal Action Segmentation
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Segment activities/actions temporally
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MohsenFayyaz89/SCT
공식 코드SCT: Set Constrained Temporal Transformer for Set Supervised Action Segmentation
https://github.com/QingZhong1996/OnlineTAS
공식 코드OnlineTAS: An Online Baseline for Temporal Action Segmentation
https://github.com/ShangHua-Gao/RFNext
공식 코드RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks
https://github.com/Thinksky5124/SVTAS
공식 코드https://github.com/ZheLi2020/TimestampActionSeg
공식 코드https://github.com/alexanderrichard/squirrel
공식 코드Weakly Supervised Action Learning with RNN based Fine-to-coarse Modeling
https://github.com/atlas-eccv22/awesome-temporal-action-segmentation
공식 코드Temporal Action Segmentation: An Analysis of Modern Techniques
https://github.com/boschresearch/uvast
공식 코드https://github.com/dipika-singhania/ICC-Semi-Supervised-TAS
공식 코드Iterative Contrast-Classify For Semi-supervised Temporal Action Segmentation
https://github.com/fedespu/hvq
공식 코드Hierarchical Vector Quantization for Unsupervised Action Segmentation
https://github.com/feixiangzhou/smc-nca
공식 코드SMC-NCA: Semantic-guided Multi-level Contrast for Semi-supervised Temporal Action Segmentation
https://github.com/gongda0e/kari
공식 코드https://github.com/hitcbw/lpl
공식 코드Language-Assisted Human Part Motion Learning for Skeleton-Based Temporal Action Segmentation
https://github.com/kimihiroh/promqa
공식 코드ProMQA: Question Answering Dataset for Multimodal Procedural Activity Understanding
https://github.com/laurenok24/nsaqa
공식 코드Hierarchical NeuroSymbolic Approach for Comprehensive and Explainable Action Quality Assessment
https://github.com/ltcontext/ltcontext
공식 코드How Much Temporal Long-Term Context is Needed for Action Segmentation?
https://github.com/nus-cvml/awesome-temporal-action-segmentation
공식 코드Temporal Action Segmentation: An Analysis of Modern Techniques
https://github.com/pangzhan27/CSL_LT-TAS
공식 코드Cost-Sensitive Learning for Long-Tailed Temporal Action Segmentation
https://github.com/pangzhan27/GTLA
공식 코드Long-Tail Temporal Action Segmentation with Group-wise Temporal Logit Adjustment
https://github.com/peiyao-w/baformer
공식 코드Efficient Temporal Action Segmentation via Boundary-aware Query Voting
https://github.com/ryota-skating/fs-jump3d
공식 코드https://github.com/sthoduka/handover_failure_detection
공식 코드A Multimodal Handover Failure Detection Dataset and Baselines
https://github.com/yabufarha/ms-tcn
공식 코드MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation
https://github.com/yiskw713/asrf
공식 코드Alleviating Over-segmentation Errors by Detecting Action Boundaries
https://github.com/yl596/otas
공식 코드OTAS: Unsupervised Boundary Detection for Object-Centric Temporal Action Segmentation
https://github.com/MindSpore-paper-code-2/code3/tree/main/TCN
MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation
https://github.com/ParitoshParmar/Fitness-AQA
Hierarchical NeuroSymbolic Approach for Comprehensive and Explainable Action Quality Assessment
https://github.com/ParitoshParmar/MTL-AQA
Hierarchical NeuroSymbolic Approach for Comprehensive and Explainable Action Quality Assessment
https://github.com/ShangHua-Gao/G2L-search
RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks
https://github.com/finspire13/diffact
https://github.com/mingu6/action_seg_ot
Temporal Action Segmentation: An Analysis of Modern Techniques
https://github.com/sj-li/MS-TCN2
MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation
https://github.com/yz-cnsdqz/ms-tcn-bilinear
Frontal Low-rank Random Tensors for Fine-grained Action Segmentation
https://github.services.devops.takamol.support/boschresearch/UVAST
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.