Papers with Code 아카이브
Zero-Shot Action Recognition
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/KAIST-VICLab/TDSM
공식 코드TDSM: Triplet Diffusion for Skeleton-Text Matching in Zero-Shot Action Recognition
https://github.com/MCG-NJU/CPD-Video
공식 코드Learning Spatiotemporal Features via Video and Text Pair Discrimination
https://github.com/Sha-Lab/CMHSE
공식 코드https://github.com/ShipuLoveMili/CVPR2022-AURL
공식 코드Alignment-Uniformity aware Representation Learning for Zero-shot Video Classification
https://github.com/azzh1/purls
공식 코드Part-aware Unified Representation of Language and Skeleton for Zero-shot Action Recognition
https://github.com/baaivision/eva
공식 코드https://github.com/bbrattoli/ZeroShotVideoClassification
공식 코드Rethinking Zero-shot Video Classification: End-to-end Training for Realistic Applications
https://github.com/bryant1410/fitclip
공식 코드FitCLIP: Refining Large-Scale Pretrained Image-Text Models for Zero-Shot Video Understanding Tasks
https://github.com/bryant1410/tclip
공식 코드FitCLIP: Refining Large-Scale Pretrained Image-Text Models for Zero-Shot Video Understanding Tasks
https://github.com/carlobretti/object-scene-compositions-for-actions
공식 코드Zero-Shot Action Recognition from Diverse Object-Scene Compositions
https://github.com/kini5gowda/TruZe
공식 코드A New Split for Evaluating True Zero-Shot Action Recognition
https://github.com/kini5gowda/stories
공식 코드Telling Stories for Common Sense Zero-Shot Action Recognition
https://github.com/lzrobots/DeepEmbeddingModel_ZSL
공식 코드https://github.com/m-bain/clip-hitchhiker
공식 코드https://github.com/mia-yatingyu/stdd
공식 코드Building a Multi-modal Spatiotemporal Expert for Zero-shot Action Recognition with CLIP
https://github.com/microsoft/VideoX/tree/master/X-CLIP
공식 코드Expanding Language-Image Pretrained Models for General Video Recognition
https://github.com/microsoft/videox
공식 코드Expanding Language-Image Pretrained Models for General Video Recognition
https://github.com/mondalanindya/msqnet
공식 코드Actor-agnostic Multi-label Action Recognition with Multi-modal Query
https://github.com/naraysa/gzsl-od
공식 코드Out-of-Distribution Detection for Generalized Zero-Shot Action Recognition
https://github.com/naver-ai/tc-clip
공식 코드Leveraging Temporal Contextualization for Video Action Recognition
https://github.com/opengvlab/internvideo
공식 코드InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation
https://github.com/pku-yuangroup/languagebind
공식 코드https://github.com/sallymmx/actionclip
공식 코드https://github.com/secure-and-intelligent-systems-lab/semanticvideotransformer
공식 코드End-to-End Semantic Video Transformer for Zero-Shot Action Recognition
https://github.com/shahzadnit/ez-clip
공식 코드https://github.com/sweetorangezhuyan/mm2023_oti
공식 코드Orthogonal Temporal Interpolation for Zero-Shot Video Recognition
https://github.com/talalwasim/vita-clip
공식 코드Vita-CLIP: Video and text adaptive CLIP via Multimodal Prompting
https://github.com/tencentarc/mcq
공식 코드Bridging Video-text Retrieval with Multiple Choice Questions
https://github.com/tomchen-ctj/OST
공식 코드OST: Refining Text Knowledge with Optimal Spatio-Temporal Descriptor for General Video Recognition
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/whwu95/BIKE
공식 코드https://github.com/whwu95/text4vis
공식 코드Revisiting Classifier: Transferring Vision-Language Models for Video Recognition
https://github.com/wlin-at/maxi
공식 코드https://github.com/CristianoPatricio/zsl-methods
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.