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Action Anticipation
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Next action anticipation is defined as observing 1, ... , T frames and predicting the action that happens after a gap of T_a seconds. It is important to note that a new action starts after T_a seconds that is not seen in the observed frames. Here T_a=1 second.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/2tianyao1/actionllm
공식 코드https://github.com/ADiko1997/S-GEAR
공식 코드Semantically Guided Representation Learning For Action Anticipation
https://github.com/CorcovadoMing/HORST
공식 코드Higher Order Recurrent Space-Time Transformer for Video Action Prediction
https://github.com/HimangiM/RepLAI
공식 코드Learning State-Aware Visual Representations from Audible Interactions
https://github.com/PanasonicConnect/ego4d_lta_ens_srw_seqdep_verb_noun_cooc
공식 코드Technical Report for Ego4D Long Term Action Anticipation Challenge 2023
https://github.com/ParitoshParmar/HalluciNet
공식 코드HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN
https://github.com/aras62/SF-GRU
공식 코드Pedestrian Action Anticipation using Contextual Feature Fusion in Stacked RNNs
https://github.com/assembly101/assembly101.github.io
공식 코드Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities
https://github.com/brown-palm/AntGPT
공식 코드AntGPT: Can Large Language Models Help Long-term Action Anticipation from Videos?
https://github.com/brown-palm/ObjectPrompt
공식 코드Object-centric Video Representation for Long-term Action Anticipation
https://github.com/dandoge/palm
공식 코드https://github.com/decisionforce/VTHCL
공식 코드https://github.com/dibschat/tempAgg
공식 코드Temporal Aggregate Representations for Long-Range Video Understanding
https://github.com/dongzhikang/mambavl
공식 코드https://github.com/epic-kitchens/epic-kitchens-100-annotations
공식 코드https://github.com/epic-kitchens/epic-kitchens-100-narrator
공식 코드https://github.com/evm7/ego4dlta-icvae
공식 코드Intention-Conditioned Long-Term Human Egocentric Action Forecasting
https://github.com/facebookresearch/AVT
공식 코드https://github.com/facebookresearch/memvit
공식 코드MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition
https://github.com/facebookresearch/vjepa2
공식 코드V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
https://github.com/fpv-iplab/easg
공식 코드Action Scene Graphs for Long-Form Understanding of Egocentric Videos
https://github.com/fpv-iplab/rulstm
공식 코드https://github.com/guxiao0822/trans_action
공식 코드TransAction: ICL-SJTU Submission to EPIC-Kitchens Action Anticipation Challenge 2021
https://github.com/linuxsino/arr
공식 코드From Recognition to Prediction: Leveraging Sequence Reasoning for Action Anticipation
https://github.com/monjurulkarim/DSTA
공식 코드A Dynamic Spatial-temporal Attention Network for Early Anticipation of Traffic Accidents
https://github.com/nmegha2601/anticipatr
공식 코드Rethinking Learning Approaches for Long-Term Action Anticipation
https://github.com/olga-zats/goal_consistency
공식 코드https://github.com/olga-zats/gtda
공식 코드Gated Temporal Diffusion for Stochastic Long-Term Dense Anticipation
https://github.com/olivesgatech/DARai
공식 코드Hierarchical and Multimodal Data for Daily Activity Understanding
https://github.com/opengvlab/egoexolearn
공식 코드https://github.com/opengvlab/egovideo
공식 코드EgoVideo: Exploring Egocentric Foundation Model and Downstream Adaptation
https://github.com/quiil/quiil_thompson_solution
공식 코드https://github.com/sanketsans/ganov2
공식 코드Enhancing Next Active Object-based Egocentric Action Anticipation with Guided Attention
https://github.com/srijandas07/clip_baseline_lta_ego4d
공식 코드Video + CLIP Baseline for Ego4D Long-term Action Anticipation
https://github.com/stonybrooknlp/action-anticipation-lmtovideo
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.