The current state-of-the-art in image segmentation includes models like Mask R-CNN, DeepLabV3+, and Vision Transformers (ViTs) for segmentation. These models leverage advanced architectures, such as attention mechanisms and atrous convolutions, to achieve high accuracy on benchmark datasets like COCO and Pascal VOC. Vision Transformers have gained prominence for their ability to capture global context and handle large-scale datasets. Research continues to improve segmentation models in terms of accuracy, efficiency, and generalizability.
Which is the current state of the art in image segmentation?
- Information Retrieval 101
- Embedding 101
- AI & Machine Learning
- GenAI Ecosystem
- Optimizing Your RAG Applications: Strategies and Methods
- All learn series →
Recommended AI Learn Series
VectorDB for GenAI Apps
Zilliz Cloud is a managed vector database perfect for building GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
How do vector embeddings support personalization?
Vector embeddings support personalization by capturing and representing the characteristics of data in a way that makes
How might Amazon Bedrock be used to power data analytics or business intelligence tools by generating natural language explanations or summaries of data findings?
Amazon Bedrock can enhance data analytics and business intelligence (BI) tools by automating the creation of natural lan
What techniques are used for anomaly detection?
Anomaly detection is the process of identifying data points that deviate significantly from the norm in a dataset. Sever