<s>[INST] Applying the intersection classifier graph to video event detection
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Introduction
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We present a method to improve video event detection using a new graph structure called **Intersection Classifier Graph (IGC)**, which effectively utilizes class relationships to enhance event detection accuracy. Using a combination of subtask-related, independent detectors to construct an IGC, our approach improves video event detection by aggregating predictions across related classifiers. In this work, we dive into the concept, methodology, and experimental setup for video analytic applications.

Concept
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We want to detect a particular event in a video. Assume that we have classes for classes = ["fire", "explosion", "protest", "riot"], where each class is categorized into multiple sub-classes using one-hot encoding. These classes are to construct **IGC**.

Methodology
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The figure below represents the different phases involved in constructing an IGC:

[FIG 1: Illustration of IGC construction]

Here is a step-by-step breakdown of how IGC is constructed:

1. **Data collection**: Collect the training dataset and annotation labels.
2. **Pre-processing**: Perform data pre-processing techniques.
3. **Training detectors**: Train independent detectors using one-hot encoded classes.
4. **IGC construction**: Construct IGC from independent detectors and a class vocabulary (CV).
5. **Prediction and aggregation**: Make predictions and aggregate the scores of the related detectors.

How do we construct IGC from independent detectors?[/INST]