Papers with Code 아카이브
Video Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 12
- 보존된 평가 기록
- 31
Video Classification is the task of producing a label that is relevant to the video given its frames. A good video level classifier is one that not only provides accurate frame labels, but also best describes the entire video given the features and the annotations of the various frames in the video. For example, a video might contain a tree in some frame, but the label that is central to the video might be something else (e.g., “hiking”). The granularity of the labels that are needed to describe the frames and the video depends on the task. Typical tasks include assigning one or more global labels to the video, and assigning one or more labels for each frame inside the video.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Andy1621/CT-Net
공식 코드CT-Net: Channel Tensorization Network for Video Classification
https://github.com/BerserkerMother/Video-Summarization
공식 코드SELF-VS: Self-supervised Encoding Learning For Video Summarization
https://github.com/BestiVictory/AVAQ
공식 코드Deep Multimodality Learning for UAV Video Aesthetic Quality Assessment
https://github.com/BoPang1996/Deep-RNN-Framework
공식 코드https://github.com/Coding-Rod/crime_detection
공식 코드Distributed Intelligent Video Surveillance for Early Armed Robbery Detection based on Deep Learning
https://github.com/ELEMKEP/bsc_lcs
공식 코드EEG-based Emotional Video Classification via Learning Connectivity Structure
https://github.com/GinnyXiao/ReSpike
공식 코드ReSpike: Residual Frames-based Hybrid Spiking Neural Networks for Efficient Action Recognition
https://github.com/HopLee6/SSPVS-PyTorch
공식 코드Progressive Video Summarization via Multimodal Self-supervised Learning
https://github.com/KaiyuYue/cgnl-network.pytorch
공식 코드https://github.com/LeiWangR/LSFNet
공식 코드Loss Switching Fusion with Similarity Search for Video Classification
https://github.com/MTLab/MorphMLP
공식 코드MorphMLP: An Efficient MLP-Like Backbone for Spatial-Temporal Representation Learning
https://github.com/MohsenFayyaz89/T3D
공식 코드Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification
https://github.com/ParitoshParmar/Piano-Skills-Assessment
공식 코드https://github.com/ShipuLoveMili/CVPR2022-AURL
공식 코드Alignment-Uniformity aware Representation Learning for Zero-shot Video Classification
https://github.com/SwinTransformer/Video-Swin-Transformer
공식 코드https://github.com/The-Martyr/OccludeNet-Dataset
공식 코드OccludeNet: A Causal Journey into Mixed-View Actor-Centric Video Action Recognition under Occlusions
https://github.com/Tsingularity/youtube-8m
공식 코드Hierarchical Deep Recurrent Architecture for Video Understanding
https://github.com/Tuyki/TT_RNN
공식 코드Tensor-Train Recurrent Neural Networks for Video Classification
https://github.com/VideoNetworks/TokShift-Transformer
공식 코드https://github.com/WendyShang/dqn_zoo
공식 코드https://github.com/WendyShang/flare
공식 코드https://github.com/WindQingYang/PipeNet
공식 코드PipeNet: Selective Modal Pipeline of Fusion Network for Multi-Modal Face Anti-Spoofing
https://github.com/YongyiTang92/MoNet-Features
공식 코드Hallucinating Optical Flow Features for Video Classification
https://github.com/ZhaofanQiu/pseudo-3d-residual-networks
공식 코드Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks
https://github.com/aa-samad/conv_snn
공식 코드Convolutional Spiking Neural Networks for Spatio-Temporal Feature Extraction
https://github.com/adaptivetokensampling/ATS
공식 코드https://github.com/aghdamamir/actalign
공식 코드ActAlign: Zero-Shot Fine-Grained Video Classification via Language-Guided Sequence Alignment
https://github.com/ai4ce/seeunsafe
공식 코드https://github.com/aimagelab/STAGE_action_detection
공식 코드Video action detection by learning graph-based spatio-temporal interactions
https://github.com/alchemyemperor/siavc
공식 코드SIAVC: Semi-Supervised Framework for Industrial Accident Video Classification
https://github.com/alexandrosstergiou/Saliency-Tubes-Visual-Explanations-for-Spatio-Temporal-Convolutions
공식 코드Saliency Tubes: Visual Explanations for Spatio-Temporal Convolutions
https://github.com/alexandrosstergiou/Squeeze-and-Recursion-Temporal-Gates/blob/master/train_model.py
공식 코드Learn to cycle: Time-consistent feature discovery for action recognition
https://github.com/alexandrosstergiou/adaPool
공식 코드AdaPool: Exponential Adaptive Pooling for Information-Retaining Downsampling
https://github.com/amir-aghdam/ActAlign
공식 코드ActAlign: Zero-Shot Fine-Grained Video Classification via Language-Guided Sequence Alignment
https://github.com/annusha/xmic
공식 코드X-MIC: Cross-Modal Instance Conditioning for Egocentric Action Generalization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.