https://github.com/pykale/pykale
공식 코드PyKale: Knowledge-Aware Machine Learning from Multiple Sources in Python
Papers with Code 아카이브
Video Recognition
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Video Recognition is a process of obtaining, processing, and analysing data that it receives from a visual source, specifically video.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/riotu-lab/DrifNet
공식 코드DriftNet: Aggressive Driving Behavior Classification using 3D EfficientNet Architecture
https://github.com/robinzixuan/Video-Question-Answering-HRI
공식 코드Open-Ended Multi-Modal Relational Reasoning for Video Question Answering
https://github.com/sail-sg/dualformer
공식 코드DualFormer: Local-Global Stratified Transformer for Efficient Video Recognition
https://github.com/shaoyuanlo/OUDefend
공식 코드https://github.com/shark0-0/vg4d
공식 코드https://github.com/sofiabroome/painface-recognition
공식 코드https://github.com/swathikirans/gsf
공식 코드https://github.com/sweetorangezhuyan/mm2023_oti
공식 코드Orthogonal Temporal Interpolation for Zero-Shot Video Recognition
https://github.com/taeoh-kim/temporal_data_augmentation
공식 코드Learning Temporally Invariant and Localizable Features via Data Augmentation for Video Recognition
https://github.com/talalwasim/video-focalnets
공식 코드Video-FocalNets: Spatio-Temporal Focal Modulation for Video Action Recognition
https://github.com/tensorflow/models
공식 코드MoViNets: Mobile Video Networks for Efficient Video Recognition
https://github.com/tobyperrett/lmr-release
공식 코드https://github.com/tomchen-ctj/OST
공식 코드OST: Refining Text Knowledge with Optimal Spatio-Temporal Descriptor for General Video Recognition
https://github.com/uchiyama33/aosa
공식 코드Adaptive occlusion sensitivity analysis for visually explaining video recognition networks
https://github.com/ucsc-vlaa/image-pretraining-for-video
공식 코드In Defense of Image Pre-Training for Spatiotemporal Recognition
https://github.com/villawang/CatNet
공식 코드CatNet: Class Incremental 3D ConvNets for Lifelong Egocentric Gesture Recognition
https://github.com/wangjk666/stts
공식 코드Efficient Video Transformers with Spatial-Temporal Token Selection
https://github.com/weiguopian/av-cil_iccv2023
공식 코드https://github.com/wengzejia1/genrec
공식 코드GenRec: Unifying Video Generation and Recognition with Diffusion Models
https://github.com/wengzejia1/open-vclip
공식 코드Open-VCLIP: Transforming CLIP to an Open-vocabulary Video Model via Interpolated Weight Optimization
https://github.com/whwu95/ATM
공식 코드What Can Simple Arithmetic Operations Do for Temporal Modeling?
https://github.com/whwu95/BIKE
공식 코드https://github.com/whwu95/DSANet
공식 코드DSANet: Dynamic Segment Aggregation Network for Video-Level Representation Learning
https://github.com/whwu95/MVFNet
공식 코드MVFNet: Multi-View Fusion Network for Efficient Video Recognition
https://github.com/whwu95/text4vis
공식 코드Revisiting Classifier: Transferring Vision-Language Models for Video Recognition
https://github.com/wlin-at/maxi
공식 코드https://github.com/wqtwjt1996/sum-l
공식 코드Learning from Semantic Alignment between Unpaired Multiviews for Egocentric Video Recognition
https://github.com/xianyongqin/few-shot-video-classification
공식 코드Generalized Few-Shot Video Classification with Video Retrieval and Feature Generation
https://github.com/xiaobul/omniclip
공식 코드OmniCLIP: Adapting CLIP for Video Recognition with Spatial-Temporal Omni-Scale Feature Learning
https://github.com/xinghaochen/squeezetime
공식 코드No Time to Waste: Squeeze Time into Channel for Mobile Video Understanding
https://github.com/yulupan00/basket
공식 코드BASKET: A Large-Scale Video Dataset for Fine-Grained Skill Estimation
https://github.com/zhaofanqiu/optimization-planning-for-3d-convnets
공식 코드https://github.com/zhaoyue-zephyrus/testra
공식 코드Real-time Online Video Detection with Temporal Smoothing Transformers
https://github.com/zhipeng-wei/tt
공식 코드Boosting the Transferability of Video Adversarial Examples via Temporal Translation
https://github.com/zhouds1918/posmlp_video
공식 코드PosMLP-Video: Spatial and Temporal Relative Position Encoding for Efficient Video Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.