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?
- Evaluating Your RAG Applications: Methods and Metrics
- Optimizing Your RAG Applications: Strategies and Methods
- Large Language Models (LLMs) 101
- The Definitive Guide to Building RAG Apps with LangChain
- The Definitive Guide to Building RAG Apps with LlamaIndex
- 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 I handle large inputs when calling the OpenAI API?
When calling the OpenAI API with large inputs, it’s essential to manage the size of your requests effectively. The API h
How do recurrent neural networks (RNNs) work?
RNNs work by processing sequential data, where the output of each step depends on the input at the current step and the
How does anomaly detection work in social network analysis?
Anomaly detection in social network analysis involves identifying unusual patterns or behaviors within a network that de