Leveraging General Foundation Models for Efficient Multi-species Coral Segmentation with Sparse Annotations

Core Concepts

Leveraging the denoised DINOv2 foundation model and a simple K-Nearest Neighbors approach, combined with a human-in-the-loop labeling regime, significantly improves the efficiency and accuracy of point label propagation for multi-species coral segmentation, especially when only a small number of labels are available.

Abstract

This paper proposes a novel approach for point label propagation in multi-species coral imagery that leverages the general DINOv2 foundation model without any fine-tuning. The key highlights are:

The denoised DINOv2 features, combined with a simple K-Nearest Neighbors (KNN) algorithm, outperform prior state-of-the-art methods for generating augmented ground truth masks from sparse point labels. This removes the need for complex custom-designed superpixel algorithms.

For extremely sparse point labels (5-25 per image), the authors introduce a human-in-the-loop labeling regime that combines the model's introspective uncertainty with human expert knowledge to select informative point locations. This results in significant improvements of up to 22.6% in mean IoU compared to prior approaches.

Even without the human-in-the-loop labeling, using the denoised DINOv2 features with KNN improves upon prior work, achieving 3.5% higher pixel accuracy and 5.7% higher mean IoU when only 5 point labels are available per image.

The authors provide a detailed analysis on the impact of the number of point labels and the point labeling style (random vs. grid) on the point propagation task, offering meaningful recommendations for efficient annotation.

Overall, this work demonstrates the relevance of general foundation models for complex domain-specific tasks, and significantly improves the efficiency and accuracy of point label propagation for multi-species coral segmentation, especially in the extremely sparse label setting.

Stats

The paper reports the following key metrics:

Pixel Accuracy (PA) of 55.72% / 64.51% / 75.07% / 88.77% for 5 / 10 / 25 / 300 random point labels respectively.
Mean Pixel Accuracy (mPA) of 39.94% / 50.91% / 65.80% / 83.84% for 5 / 10 / 25 / 300 random point labels respectively.
Mean Intersection over Union (mIoU) of 32.09% / 42.79% / 58.04% / 81.75% for 5 / 10 / 25 / 300 random point labels respectively.

Quotes

"If only 5 point labels per image are available, our proposed human-in-the-loop approach improves on the state-of-the-art by 17.3% for pixel accuracy and 22.6% for mIoU; and by 10.6% and 19.1% when 10 point labels per image are available."
"Even if the human-in-the-loop labeling regime is not used, the denoised DINOv2 features with a KNN outperforms the prior state-of-the-art by 3.5% for pixel accuracy and 5.7% for mIoU (5 grid points)."

Key Insights Distilled From

Human-in-the-Loop Segmentation of Multi-species Coral Imagery

by Scarlett Rai... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09406.pdf

Deeper Inquiries

How could the proposed human-in-the-loop labeling regime be extended to other computer vision tasks beyond coral segmentation

The proposed human-in-the-loop labeling regime can be extended to other computer vision tasks by adapting the concept of leveraging human expertise to improve the accuracy and efficiency of model predictions. For tasks like object detection, image classification, or instance segmentation, the human-in-the-loop approach can involve selecting key points or regions of interest in the images to provide guidance to the model. This can help in focusing the model's attention on critical areas, reducing ambiguity, and improving overall performance. Additionally, the human-in-the-loop regime can be used for data augmentation, quality control, and model validation in various computer vision applications.

What are the potential limitations of relying solely on a general foundation model like DINOv2 for domain-specific tasks, and how could these be addressed

Relying solely on a general foundation model like DINOv2 for domain-specific tasks may have some limitations. One potential limitation is the lack of fine-tuning on specific domain data, which can result in suboptimal performance for tasks that require specialized knowledge or features. To address this limitation, transfer learning techniques can be employed to adapt the general model to the specific domain by fine-tuning on a smaller dataset of domain-specific images. Another limitation is the model's interpretability and explainability, especially in complex tasks like coral segmentation where domain experts need to understand and trust the model's decisions. Incorporating interpretability techniques such as attention mechanisms or saliency maps can help address this limitation and improve the model's transparency.

What other types of domain knowledge could be incorporated into the point label selection process to further improve the efficiency and accuracy of the point propagation task

To further improve the efficiency and accuracy of the point label selection process in the point propagation task, additional domain knowledge can be incorporated. One approach is to integrate contextual information about the surrounding pixels or regions to guide the selection of informative points. This can involve considering spatial relationships, texture patterns, or color consistency in the vicinity of the labeled points. Domain-specific rules or constraints can also be applied to ensure that the selected points align with known characteristics of the target objects. Furthermore, incorporating feedback mechanisms where the model learns from the human expert's labeling decisions can enhance the model's understanding of the task and improve the quality of the propagated labels.