This workshop aims to explore the use of Meta’s Segment Anything Model 2.1 for efficient and precise data annotation in specialized computer vision domains. The primary objective is to enhance the segmentation and object tracking processes in video datasets by leveraging domain-specific adaptations of SAM 2.1, reducing the manual effort traditionally required for such tasks.

With the increasing demand for domain-adapted computer vision models—whether in medical imaging, environmental monitoring, or other niche areas—the need to optimize annotation workflows has become crucial. SAM 2.1 presents a flexible base model for segmentation and tracking tasks, and fine-tuning it for specific, nuanced domains can improve segmentation accuracy, particularly when applied to highly specialized or hard-to-segment objects in videos.

In this workshop, we will showcase: (1) methods to fine-tune SAM 2.1 using specific domain datasets, (2) techniques for evaluating fine-tuned model performance, and (3) mechanisms for integrating fine-tuned SAM models into real-world annotation pipelines. We aim to empower researchers and practitioners to scale their computer vision research, from object detection to activity recognition, by significantly reducing the time and resources required for manual data labeling. Additionally, we will discuss the benefits of combining automated and human-in-the-loop approaches for enhanced labeling performance in dynamic video datasets.

Ultimately, this workshop seeks to bridge the gap between general-purpose segmentation models and specialized computer vision applications, providing practical solutions for researchers dealing with complex video data. We are committed to fostering an inclusive environment with broad representation across research areas, regions, and industries, encouraging collaboration and knowledge sharing to push the boundaries of computer vision annotation technology.