Semantic segmentation is applied in scenarios requiring pixel-level understanding of images. In autonomous vehicles, it is used to identify and differentiate between road elements, such as lanes, pedestrians, and vehicles. In medical imaging, semantic segmentation helps in identifying regions of interest, such as tumors or organs, in X-rays or MRI scans. Other applications include agriculture (e.g., plant and soil segmentation), environmental monitoring (e.g., land-use classification), and video analytics (e.g., activity recognition). The ability to assign meaningful labels to each pixel makes semantic segmentation valuable in diverse domains.
Where do you apply the concept of 'semantic segmentation'?
- Information Retrieval 101
- The Definitive Guide to Building RAG Apps with LlamaIndex
- Embedding 101
- How to Pick the Right Vector Database for Your Use Case
- Exploring Vector Database Use Cases
- 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
What strategies exist to give partial responses or stream the answer as it's being generated to mask backend latency in a RAG system?
To mask backend latency in a RAG system, three main strategies can be employed: streaming generated tokens incrementally
How are LLMs optimized for memory usage?
LLMs are optimized for memory usage through techniques like model quantization, parameter sharing, and activation checkp
How do multi-agent systems simulate crowd behavior?
Multi-agent systems simulate crowd behavior by using a collection of individual agents that represent people in a crowd.