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?

- How to Pick the Right Vector Database for Your Use Case
- Natural Language Processing (NLP) Basics
- Accelerated Vector Search
- Natural Language Processing (NLP) Advanced Guide
- The Definitive Guide to Building RAG Apps with LangChain
- 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 game engines like Unity and Unreal Engine support AR projects?
Game engines like Unity and Unreal Engine offer robust support for Augmented Reality (AR) projects through comprehensive
How do speech recognition systems manage audio preprocessing?
Speech recognition systems manage audio preprocessing through a series of steps designed to enhance the quality of the i
How do multi-agent systems handle shared resources?
Multi-agent systems (MAS) handle shared resources through coordination, negotiation, and conflict resolution mechanisms.