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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/MindCode-4/code-11/tree/main/drop-an-octave-reducing-spatial

Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution

https://github.com/MindCode-4/code-12/tree/main/GAN-master

GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

https://github.com/MindCode-4/code-12/tree/main/group-normalization

Group Normalization

https://github.com/MindCode-4/code-12/tree/main/multimodal-trajectory

Multimodal Motion Prediction with Stacked Transformers

https://github.com/MindCode-4/code-13/tree/main/vat

VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text

https://github.com/MindCode-4/code-3/tree/main/florence2

Florence: A New Foundation Model for Computer Vision

https://github.com/MindCode-4/code-4/tree/main/sam

SAM 2: Segment Anything in Images and Videos

https://github.com/MindCode-4/code-5/tree/main/swin

Swin Transformer V2: Scaling Up Capacity and Resolution

https://github.com/MindCode-4/code-5/tree/main/timesformer

Is Space-Time Attention All You Need for Video Understanding?

https://github.com/MindCode-4/code-5/tree/main/tvlt

TVLT: Textless Vision-Language Transformer

https://github.com/MindCode-4/code-5/tree/main/umt5

UMT: Unified Multi-modal Transformers for Joint Video Moment Retrieval and Highlight Detection

https://github.com/MindCode-4/code-5/tree/main/vit_mae

ViC-MAE: Self-Supervised Representation Learning from Images and Video with Contrastive Masked Autoencoders

https://github.com/MindCode-4/code-5/tree/main/x_clip

X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval

https://github.com/MindCode-4/code-6/tree/main/ACGNet

ACGNet: Action Complement Graph Network for Weakly-supervised Temporal Action Localization

https://github.com/MindCode-4/code-6/tree/main/dice-loss-for-data-imbalanced-nlp-tasks

Delving Deeper into Convolutional Networks for Learning Video Representations

https://github.com/MindCode-4/code-6/tree/main/drop-an-octave-reducing-spatial

Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution

https://github.com/MindCode-4/code-7/tree/main/GAN-master

GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

https://github.com/MindCode-4/code-7/tree/main/group-normalization

Group Normalization

https://github.com/MindCode-4/code-9/tree/main/vat

VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text

https://github.com/MindSpore-paper-code-2/code2/tree/main/Non_local

Non-local Neural Networks

https://github.com/MindSpore-paper-code-2/code2/tree/main/OSVOS

Make One-Shot Video Object Segmentation Efficient Again

https://github.com/MindSpore-paper-code-2/code2/tree/main/ReIDStrongBaseline

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/MindSpore-paper-code-2/code2/tree/main/predrnn%2B%2B

PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning

https://github.com/MindSpore-paper-code-2/code2/tree/main/r2plus1d

Video Classification with Channel-Separated Convolutional Networks

https://github.com/MindSpore-paper-code-2/code2/tree/main/rbpn

Recurrent Back-Projection Network for Video Super-Resolution

https://github.com/MindSpore-paper-code-2/code2/tree/main/resnet3d

Revisiting 3D ResNets for Video Recognition

https://github.com/MindSpore-paper-code-2/code3/tree/main/TNT

TNT: Text-Conditioned Network with Transductive Inference for Few-Shot Video Classification

https://github.com/MindSpore-paper-code-2/code3/tree/main/stgcn

DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action Recognition

https://github.com/MindSpore-paper-code-2/code3/tree/main/stnet

StNet: Local and Global Spatial-Temporal Modeling for Action Recognition

https://github.com/MindSpore-paper-code-2/code3/tree/main/tracktor%2B%2B

Tracking without bells and whistles

https://github.com/MindSpore-paper-code-2/code3/tree/main/trn

Temporal Relational Reasoning in Videos

https://github.com/MindSpore-paper-code-2/code3/tree/main/tsm

TSM: Temporal Shift Module for Efficient and Scalable Video Understanding on Edge Device

https://github.com/MindSpore-paper-code-2/code3/tree/main/tsn

Temporal Segment Networks for Action Recognition in Videos

https://github.com/MindSpore-paper-code-3/code1/tree/main/AttentionCluster

Learnable pooling with Context Gating for video classification

https://github.com/MindSpore-paper-code-3/code10/tree/main/fairmot

FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

https://github.com/MindSpore-paper-code-3/code10/tree/main/flownet2

FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.