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?

- Exploring Vector Database Use Cases
- The Definitive Guide to Building RAG Apps with LangChain
- Optimizing Your RAG Applications: Strategies and Methods
- Mastering Audio AI
- Getting Started with Zilliz Cloud
- 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 LLM guardrails work with token-level filtering?
LLM guardrails work with token-level filtering by analyzing and filtering out specific tokens (or words) in a response t
How do AI agents use decision-making processes?
AI agents utilize decision-making processes to assess situations, evaluate options, and choose actions based on predeter
How do you migrate legacy systems to the cloud?
Migrating legacy systems to the cloud involves several strategic steps to ensure a smooth transition while minimizing di