Efficient Instance Segmentation Framework for Sport-scenes with Memory Efficiency-Oriented Approach

Core Concepts

Efficient instance segmentation framework integrating visual inductive priors enhances model performance in resource-constrained environments.

Abstract

The content discusses an efficient instance segmentation framework for sport-scenes focusing on memory efficiency. The proposed framework incorporates visual inductive priors at various stages, including data preprocessing, augmentation, and model inference. By leveraging prior knowledge from the dataset, the model achieves promising performance even under limited data and memory constraints. The methodology includes basketball court detection and cropping algorithms, identity identification based on object location, style transformation, copy-paste augmentation strategies, and inference on regions of interest. Experimental results show that the model outperforms conventional approaches in terms of performance while using significantly less memory.

Stats

Our model achieves a performance of 0.509 AP@0.50:0.95.
The image sizes of the training set are reduced by 33.98%, validation set by 33.17%, and test set by 40.72%.
Our model requires only 34.6% of the memory compared to previous models.
Inference times are reduced to 3.95 seconds.

Quotes

"Copy-paste augmentation emerges as an effective strategy for instance segmentation task."
"Our proposed algorithm can effectively determine areas where various types of objects are likely to appear."
"The experiments demonstrate that such an approach can significantly enhance model performance even in resource-constrained environments."

Key Insights Distilled From

Augment Before Copy-Paste

by Chih-Chung H... at arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.11572.pdf

Deeper Inquiries

How can increasing image size during training improve model performance

Increasing the image size during training can improve model performance by allowing the model to capture more detailed features and nuances present in the images. Larger images provide a higher resolution, enabling the model to learn intricate patterns and textures that may be crucial for accurate instance segmentation. With more information available in larger images, the model can make better-informed decisions when identifying objects and their boundaries. This enhanced level of detail can lead to improved generalization capabilities, especially when dealing with complex scenes or objects with fine details.

What are the potential drawbacks of relying solely on prior knowledge for object identification

Relying solely on prior knowledge for object identification has potential drawbacks that could impact the overall performance of an instance segmentation framework. One major drawback is limited adaptability to unseen scenarios or variations within classes. If the prior knowledge used for identification is too rigid or specific, it may struggle to accurately classify objects that deviate from expected norms or encounter novel instances not covered by existing priors. This lack of flexibility could result in misclassifications, reduced accuracy, and compromised generalization ability across diverse datasets.
Additionally, over-reliance on prior knowledge alone may hinder the framework's capacity to handle dynamic environments where object characteristics change over time or under different conditions. Without incorporating adaptive learning mechanisms alongside priors, the system might struggle to adjust its identification strategies based on evolving contexts or new data distributions.

How does the proposed framework compare to other state-of-the-art models in terms of computational resource requirements

The proposed framework demonstrates notable advantages compared to other state-of-the-art models in terms of computational resource requirements. Specifically, when comparing with previous models like Yunusov et al.'s [13] and Yan et al.'s [17], our approach showcases superior efficiency in memory consumption while maintaining competitive performance levels.
In our experiments, we observed that our model utilized only 34.6% of memory compared to Yan et al.'s [17], which consumed significantly more resources (65.6%). Despite this substantial reduction in memory usage, our framework achieved a commendable AP@0.50:0.95 score of 0.509 – showcasing efficient utilization of computational resources without compromising performance quality.
This highlights how optimizing data preprocessing stages such as cropping algorithms based on visual cues and leveraging identity-based style transformations can lead to significant improvements in both memory efficiency and inference speed while achieving promising results in challenging tasks like sport-scenes instance segmentation.