Multi-Stage Contrastive Regression for Basketball Performance Assessment

In this article, we present a novel approach to efficient procedure segmentation in diving videos called MCoRe. The problem is formulated as a contrastive regression issue, where the goal is to predict the relative score of each frame based on its similarity to the exemplar frames. Our proposed framework, MCoRe, leverages multi-stage contrastive regression to improve performance while minimizing computational complexity.
To begin with, let’s break down the problem at hand. Imagine you have a video of a diver performing various actions underwater, such as strokes or maneuvers. The task is to identify which frame belongs to which action, which can be challenging due to variations in lighting, camera angles, and motion blur. To address this issue, we employ a clever technique called contrastive regression, which allows the model to learn from both the exemplar frames (i.e., the reference frames with the same action) and the input video frames.
The core idea of MCoRe is to divide the procedure segmentation into multiple stages, each focusing on a specific aspect of the action. By doing so, we can reduce the computational complexity without sacrificing performance. The first stage involves detecting the overall motion of the diver using a 2D CNN, followed by identifying the distinct actions in each frame using a small CNN. Next, we refine the predictions by incorporating spatial and temporal information through a novel attention mechanism. Finally, we combine the outputs from all stages to produce the final relative score for each frame.
Now, let’s put this into perspective. Imagine you have a recipe book filled with various dishes, but some of them are misspelled or lacking essential ingredients. Our approach is like a talented chef who can decipher the recipes, identify the missing elements, and offer suggestions for improvement. By doing so, we can create a more accurate and efficient procedure segmentation system that can be applied to various diving scenarios.
In conclusion, MCoRe offers a lightweight and efficient approach to procedure segmentation in diving videos, which is critical in applications such as sports analytics or training. Our proposed framework leverages multi-stage contrastive regression to improve performance while minimizing computational complexity, making it an ideal solution for real-world scenarios.