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

https://github.com/escorciav/Mixture-of-Embedding-Experts

Learning a Text-Video Embedding from Incomplete and Heterogeneous Data

https://github.com/escorciav/Text-to-Clip_Retrieval

Guess Where? Actor-Supervision for Spatiotemporal Action Localization

https://github.com/escorciav/roi_pooling

Guess Where? Actor-Supervision for Spatiotemporal Action Localization

https://github.com/eth-ait/spl

Structured Prediction Helps 3D Human Motion Modelling

https://github.com/eth212/Qmind_ModelNet10_PointNet

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

https://github.com/ethanluoyc/transporter-pytorch

Unsupervised Learning of Object Keypoints for Perception and Control

https://github.com/ethvis/qd-track

QDTrack: Quasi-Dense Similarity Learning for Appearance-Only Multiple Object Tracking

https://github.com/eynaij/X-Temporal_catdim

Temporal Interlacing Network

https://github.com/ezelikman/anonymal

Fast Online Object Tracking and Segmentation: A Unifying Approach

https://github.com/f1yfisher/drivedreamer2

DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video Generation

https://github.com/fabienbaradel/cophy

CoPhy: Counterfactual Learning of Physical Dynamics

https://github.com/fabioasdias/ubao

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/facebookresearch/DetectAndTrack

Detect-and-Track: Efficient Pose Estimation in Videos

https://github.com/facebookresearch/EgoTV

CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval

https://github.com/facebookresearch/QuaterNet

On human motion prediction using recurrent neural networks

https://github.com/facebookresearch/SegmPred

Predicting Deeper into the Future of Semantic Segmentation

https://github.com/facebookresearch/SparseConvNet

Sparse 3D convolutional neural networks

https://github.com/facebookresearch/assemblyhands-toolkit

AssemblyHands: Towards Egocentric Activity Understanding via 3D Hand Pose Estimation

https://github.com/facebookresearch/detectron

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/facebookresearch/fairseq

VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding

https://github.com/facebookresearch/hiervl

HierVL: Learning Hierarchical Video-Language Embeddings

https://github.com/facebookresearch/improved_vrnn

Improved Conditional VRNNs for Video Prediction

https://github.com/facebookresearch/instpred

Predicting Future Instance Segmentation by Forecasting Convolutional Features

https://github.com/facebookresearch/multimodal

CoCa: Contrastive Captioners are Image-Text Foundation Models

https://github.com/facebookresearch/nwm

Navigation World Models

https://github.com/facebookresearch/segment-anything

SAM 2: Segment Anything in Images and Videos

https://github.com/facebookresearch/semi-supervised-ImageNet1K-models

Billion-scale semi-supervised learning for image classification

https://github.com/facebookresearch/videoalignment

Circulant temporal encoding for video retrieval and temporal alignment

https://github.com/facebookresearch/xformers

Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers

https://github.com/facundoq/transformational_measures_experiments

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/fan-aaron-zhang/cvegan

CVEGAN: A Perceptually-inspired GAN for Compressed Video Enhancement

https://github.com/fandulu/Keras-for-Co-occurrence-Feature-Learning-from-Skeleton-Data-for-Action-Recognition

Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation

https://github.com/fangwei123456/spikingjelly

HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors

https://github.com/fanshixiong/tracker

High-Speed Tracking with Kernelized Correlation Filters

https://github.com/fanzhenliu/dagad

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/farazahmeds/Classification-of-brain-tumor-using-Spatiotemporal-models

ConvNet Architecture Search for Spatiotemporal Feature Learning

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