ActivityNet
ActivityNet solver
An implementation of the 2016 ActivityNet action recognition challenge using a CNN + LSTM approach with multi-threaded loading.
2016 ActivityNet action recognition challenge. CNN + LSTM approach. Multi-threaded loading.
54 stars
3 watching
14 forks
Language: Lua
last commit: over 8 years ago
Linked from 1 awesome list
Related projects:
Repository | Description | Stars |
---|---|---|
| An implementation of a deep neural network architecture for Human Activity Recognition using stacked residual bidirectional LSTM cells with TensorFlow. | 319 |
| A Gluon implementation of Residual Attention Network for image classification tasks | 108 |
| A framework for building and testing activity recognition models using deep learning techniques. | 107 |
| A Lua-based implementation of a multi-GPU training framework for image classification using the Alexnet model. | 4 |
| A system for efficiently training and deploying neural networks for named entity recognition tasks with context-dependent representations. | 147 |
| Develops a deep neural network model for detecting salient objects in RGBT images using correlation information from other colors. | 13 |
| Implementations of deep learning architectures using PyTorch for image classification tasks on various datasets. | 112 |
| Automates the search for optimal neural network configurations in deep learning applications | 468 |
| A PyTorch implementation of an improved question answering architecture with dynamic memory networks and attention mechanisms | 64 |
| Develops CTC-based text recognition models with neural network architectures | 259 |
| An implementation of a contrastive learning approach to address noisy labels in machine learning models | 5 |
| A toolkit providing a framework and tools for designing and evaluating activity recognition systems using inertial sensors. | 56 |
| A Ruby interface to MXNet's deep learning framework | 48 |
| An extension to Torch7's neural network package with experimental modules and optimizations. | 97 |
| This project uses deep learning and Lie group theory to recognize actions from skeleton data | 64 |