Few Shot Action Recognition: An Introduction to the Computer Vision Challenge
Few shot action recognition is a computer vision problem that aims to classify an unlabelled video into one of several defined action categories. The challenge arises from the limited number of training samples available in the support set for each action class. This is often referred to as shot, which mainly depends on the size and diversity of the training dataset.
The objective of the few-shot action recognition task is to combine the knowledge of different action classes learned from a small number of samples to classify a new video with an unknown label efficiently. This challenge has been recently posed as a benchmark for evaluating the performance of deep learning models in recognizing actions in videos, considering the real-world data scarcity.
Why is Few Shot Action Recognition a Challenge for Computer Vision?
The main challenge of the few-shot action recognition is in transferring knowledge learned from a few samples of a certain class to recognize videos of previously unseen classes. In other words, the model must generalize across classes and adapt to new data. The main reason for this challenge stems from the following reasons:
- Diversity of human actions: Even with a limited action dataset, there are many variations of each action performed by different people. This high degree of diversity in human actions makes it difficult to generalize from a limited number of samples.
- Variation in recording conditions: Action videos captured from different cameras or under different lighting conditions can have varying background, viewpoint, and scale. Therefore, the model must be able to recognize the same action under different conditions.
- Data scarcity: Few-shot action recognition dataset is inherently difficult because real-world data is scarce. Video data collection is labor-intensive, and annotation is time-consuming, which makes it difficult to collect a large number of samples for each action class.
The Benefits of Few Shot Action Recognition
Few-shot action recognition is crucial for modern computer vision systems that must learn from limited examples. This approach has several benefits, including:
- Saves time and resources: Collecting a large number of labeled action videos is time-consuming, and even with a large dataset available, increasing the number of samples of each class is often difficult. Therefore, few-shot action recognition makes use of knowledge transfer from a few examples to classification of previously unseen video samples.
- Real-world applications: Few-shot action recognition has real-world applications such as security and safety systems. For example, security cameras can be used to monitor human activities in public spaces, and the system can be trained to detect suspicious activities or emergency situations.
- Efficient learning process: With few-shot learning, the model can learn from small amounts of data, which is particularly beneficial in settings where data is limited but there is a need for a fast learning process. This can potentially increase the accuracy of the model by reducing the impact of overfitting on the training data.
Deep Learning Approaches to Few Shot Action Recognition
Deep learning models have shown impressive performance in solving various computer vision tasks, including action recognition. Few-shot action recognition models mainly rely on the following two popular learning paradigms:
- Meta-learning: This approach is designed to learn how to learn efficiently from a few examples of previously unseen classes. Meta-learning frameworks like Matching Networks, Prototypical Networks, and Relation Networks are widely used in few-shot action recognition.
- Knowledge Distillation: In knowledge distillation, knowledge gained from a model with large amounts of data and resources is transferred to a smaller model with fewer resources. The model aims to mimic the behavior of larger models using a limited dataset.
Most of these models take advantage of Residual Neural Networks (ResNets) for the representation of video frames. ResNets have been proven as effective models for deep learning and have been used in various computer vision tasks.
Transfer learning for Few Shot Action Recognition
Transfer learning is a popular approach in machine learning and has also been used in few-shot action recognition. The idea is to utilize knowledge learned from previous tasks to solve new tasks that may have different but relevant characteristics. In few-shot action recognition, the transfer learning approach is mainly applied to utilizing pre-trained models on large-scale datasets, which can then be fine-tuned on smaller action recognition datasets.
For example, a pre-trained model on the ImageNet dataset, which contains millions of annotated images, can be fine-tuned on a few-shot action recognition dataset. Such pre-trained models can learn more robust representations of video frames, providing better adaptation to new classes even from only a few examples.
Few-shot action recognition is a challenging and important problem in computer vision. With the rise of deep learning techniques, promising models, such as meta-learning and knowledge distillation, are being developed to address the few-shot action recognition challenge. Additionally, transfer learning has shown to be effective in utilizing pre-trained models to improve the recognition of actions from limited datasets. With the continued development of these models, few-shot action recognition has the potential to improve the efficiency and accuracy of modern computer vision systems in real-world applications.
