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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/polimi-ispl/icpr2020dfdc
공식 코드https://github.com/prabuddha1/acpe
공식 코드https://github.com/princeton-nlp/mquake
공식 코드MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions
https://github.com/proteus1991/pscc-net
공식 코드https://github.com/prouast/ctc-intake-detection
공식 코드Single-stage intake gesture detection using CTC loss and extended prefix beam search
https://github.com/prouast/deep-intake-detection
공식 코드Learning deep representations for video-based intake gesture detection
https://github.com/psmmettes/object-priors-unseen-actions
공식 코드https://github.com/ptirupat/MLAD
공식 코드Modeling Multi-Label Action Dependencies for Temporal Action Localization
https://github.com/ptoupas/harflow3d
공식 코드HARFLOW3D: A Latency-Oriented 3D-CNN Accelerator Toolflow for HAR on FPGA Devices
https://github.com/purduedigitaltwin/seneva
공식 코드Quantifying Uncertainty in Motion Prediction with Variational Bayesian Mixture
https://github.com/pxiangwu/MotionNet
공식 코드https://github.com/pytorch/fairseq
공식 코드VLM: Task-agnostic Video-Language Model Pre-training for Video Understanding
https://github.com/qilimk/biggan-am
공식 코드https://github.com/qinghua-zhou/stealth-edits
공식 코드https://github.com/qinying-liu/c3bn
공식 코드Convex Combination Consistency between Neighbors for Weakly-supervised Action Localization
https://github.com/qinying-liu/case
공식 코드https://github.com/qiuzh20/tuning-keys-v.s.-values
공식 코드Empirical Study on Updating Key-Value Memories in Transformer Feed-forward Layers
https://github.com/qizhou000/DAFNet
공식 코드DAFNet: Dynamic Auxiliary Fusion for Sequential Model Editing in Large Language Models
https://github.com/qizhou000/RECIPE
공식 코드Lifelong Knowledge Editing for LLMs with Retrieval-Augmented Continuous Prompt Learning
https://github.com/qizhou000/visedit
공식 코드https://github.com/qualityminds/cistgcn
공식 코드Context-based Interpretable Spatio-Temporal Graph Convolutional Network for Human Motion Forecasting
https://github.com/quqxui/medlasa
공식 코드Editing Factual Knowledge and Explanatory Ability of Medical Large Language Models
https://github.com/rana2149/actnetformer
공식 코드ActNetFormer: Transformer-ResNet Hybrid Method for Semi-Supervised Action Recognition in Videos
https://github.com/rate-accuracy-mvcnn/main
공식 코드Rate-Accuracy Trade-Off In Video Classification With Deep Convolutional Neural Networks
https://github.com/rd20karim/m2t-interpretable
공식 코드https://github.com/rd20karim/synch-transformer
공식 코드Transformer with Controlled Attention for Synchronous Motion Captioning
https://github.com/realcrane/SMART
공식 코드Understanding the Robustness of Skeleton-based Action Recognition under Adversarial Attack
https://github.com/reddyav1/rocog-v2
공식 코드Synthetic-to-Real Domain Adaptation for Action Recognition: A Dataset and Baseline Performances
https://github.com/reddyav1/unite
공식 코드Unsupervised Video Domain Adaptation with Masked Pre-Training and Collaborative Self-Training
https://github.com/redwang/DTGRM
공식 코드Temporal Relational Modeling with Self-Supervision for Action Segmentation
https://github.com/rezaghoddoosian/hierarchical-task-modeling
공식 코드https://github.com/richardtml/divita
공식 코드https://github.com/ricky-xian/mitfas
공식 코드https://github.com/ricky-xian/pmi-sampler
공식 코드PMI Sampler: Patch Similarity Guided Frame Selection for Aerial Action Recognition
https://github.com/rmokady/JOKR
공식 코드JOKR: Joint Keypoint Representation for Unsupervised Cross-Domain Motion Retargeting
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.