ActionvladActionVLAD for video action classification (CVPR 2017)
Stars: ✭ 217 (+471.05%)
conv3d-video-action-recognitionMy experimentation around action recognition in videos. Contains Keras implementation for C3D network based on original paper "Learning Spatiotemporal Features with 3D Convolutional Networks", Tran et al. and it includes video processing pipelines coded using mPyPl package. Model is being benchmarked on popular UCF101 dataset and achieves result…
Stars: ✭ 50 (+31.58%)
pose2actionexperiments on classifying actions using poses
Stars: ✭ 24 (-36.84%)
TCEThis repository contains the code implementation used in the paper Temporally Coherent Embeddings for Self-Supervised Video Representation Learning (TCE).
Stars: ✭ 51 (+34.21%)
DEAR[ICCV 2021 Oral] Deep Evidential Action Recognition
Stars: ✭ 36 (-5.26%)
DIN-Group-Activity-Recognition-BenchmarkA new codebase for Group Activity Recognition. It contains codes for ICCV 2021 paper: Spatio-Temporal Dynamic Inference Network for Group Activity Recognition and some other methods.
Stars: ✭ 26 (-31.58%)
Tsn PytorchTemporal Segment Networks (TSN) in PyTorch
Stars: ✭ 895 (+2255.26%)
C3D-tensorflowAction recognition with C3D network implemented in tensorflow
Stars: ✭ 34 (-10.53%)
Pose2vecA Repository for maintaining various human skeleton preprocessing steps in numpy and tensorflow along with tensorflow model to learn pose embeddings.
Stars: ✭ 25 (-34.21%)
Movienet ToolsTools for movie and video research
Stars: ✭ 113 (+197.37%)
MmactionAn open-source toolbox for action understanding based on PyTorch
Stars: ✭ 1,711 (+4402.63%)
I3d finetuneTensorFlow code for finetuning I3D model on UCF101.
Stars: ✭ 128 (+236.84%)
Robust-Deep-Learning-PipelineDeep Convolutional Bidirectional LSTM for Complex Activity Recognition with Missing Data. Human Activity Recognition Challenge. Springer SIST (2020)
Stars: ✭ 20 (-47.37%)
Mmaction2OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark
Stars: ✭ 684 (+1700%)
cpnetLearning Video Representations from Correspondence Proposals (CVPR 2019 Oral)
Stars: ✭ 93 (+144.74%)
StepSTEP: Spatio-Temporal Progressive Learning for Video Action Detection. CVPR'19 (Oral)
Stars: ✭ 196 (+415.79%)
PaddlevideoComprehensive, latest, and deployable video deep learning algorithm, including video recognition, action localization, and temporal action detection tasks. It's a high-performance, light-weight codebase provides practical models for video understanding research and application
Stars: ✭ 218 (+473.68%)
iPerceiveApplying Common-Sense Reasoning to Multi-Modal Dense Video Captioning and Video Question Answering | Python3 | PyTorch | CNNs | Causality | Reasoning | LSTMs | Transformers | Multi-Head Self Attention | Published in IEEE Winter Conference on Applications of Computer Vision (WACV) 2021
Stars: ✭ 52 (+36.84%)
Patient2VecPatient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Record
Stars: ✭ 85 (+123.68%)
torch-lrcnAn implementation of the LRCN in Torch
Stars: ✭ 85 (+123.68%)
video autoencoderVideo lstm auto encoder built with pytorch. https://arxiv.org/pdf/1502.04681.pdf
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Tdn[CVPR 2021] TDN: Temporal Difference Networks for Efficient Action Recognition
Stars: ✭ 72 (+89.47%)
Video ClassificationTutorial for video classification/ action recognition using 3D CNN/ CNN+RNN on UCF101
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LintelA Python module to decode video frames directly, using the FFmpeg C API.
Stars: ✭ 240 (+531.58%)
temporal-sslVideo Representation Learning by Recognizing Temporal Transformations. In ECCV, 2020.
Stars: ✭ 46 (+21.05%)
Learning-From-RulesImplementation of experiments in paper "Learning from Rules Generalizing Labeled Exemplars" to appear in ICLR2020 (https://openreview.net/forum?id=SkeuexBtDr)
Stars: ✭ 46 (+21.05%)
dnd-lstmA Python(PyTorch) implementation of memory augmented neural network based on Ritter et al. (2018). Been There, Done That: Meta-Learning with Episodic Recall. ICML.
Stars: ✭ 30 (-21.05%)
FUSIONPyTorch code for NeurIPSW 2020 paper (4th Workshop on Meta-Learning) "Few-Shot Unsupervised Continual Learning through Meta-Examples"
Stars: ✭ 18 (-52.63%)
mediapipe plusThe purpose of this project is to apply mediapipe to more AI chips.
Stars: ✭ 38 (+0%)
Time-Series-ForecastingRainfall analysis of Maharashtra - Season/Month wise forecasting. Different methods have been used. The main goal of this project is to increase the performance of forecasted results during rainy seasons.
Stars: ✭ 27 (-28.95%)
PCC-pytorchA pytorch implementation of the paper "Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control"
Stars: ✭ 57 (+50%)
MSAFOffical implementation of paper "MSAF: Multimodal Split Attention Fusion"
Stars: ✭ 47 (+23.68%)
tatorVideo analytics web platform
Stars: ✭ 66 (+73.68%)
MUSES[CVPR 2021] Multi-shot Temporal Event Localization: a Benchmark
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Action-LocalizationAction-Localization, Atomic Visual Actions (AVA) Dataset
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viddlRuby/Command line tool to download, cut, crop and resize video clips
Stars: ✭ 32 (-15.79%)
VideoTransformer-pytorchPyTorch implementation of a collections of scalable Video Transformer Benchmarks.
Stars: ✭ 159 (+318.42%)
causal-mlMust-read papers and resources related to causal inference and machine (deep) learning
Stars: ✭ 387 (+918.42%)
anatomeἈνατομή is a PyTorch library to analyze representation of neural networks
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image embeddingsUsing efficientnet to provide embeddings for retrieval
Stars: ✭ 107 (+181.58%)
DrowsyDriverDetectionThis is a project implementing Computer Vision and Deep Learning concepts to detect drowsiness of a driver and sound an alarm if drowsy.
Stars: ✭ 82 (+115.79%)
ViCC[WACV'22] Code repository for the paper "Self-supervised Video Representation Learning with Cross-Stream Prototypical Contrasting", https://arxiv.org/abs/2106.10137.
Stars: ✭ 33 (-13.16%)
event-embedding-multitask*SEM 2018: Learning Distributed Event Representations with a Multi-Task Approach
Stars: ✭ 22 (-42.11%)
PC3-pytorchPredictive Coding for Locally-Linear Control (ICML-2020)
Stars: ✭ 16 (-57.89%)
dnn.coolA framework for multi-task learning, where you may precondition tasks and compose them into bigger tasks. Conditional objectives and per-task evaluations and interpretations.
Stars: ✭ 44 (+15.79%)
glimpse cloudsPytorch implementation of the paper "Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points", F. Baradel, C. Wolf, J. Mille , G.W. Taylor, CVPR 2018
Stars: ✭ 30 (-21.05%)