Rich Embedding Features for One-Shot Semantic Segmentation.

Area of Science:

• Computer Vision
• Machine Learning
• Artificial Intelligence

Background:

• One-shot semantic segmentation requires segmenting objects from novel categories using only one annotated example.
• Effective feature representation from guidance images is critical for one-shot semantic segmentation success.
• Existing methods struggle with robust feature extraction from limited data.

Purpose of the Study:

• To propose a novel approach for one-shot semantic segmentation using Rich Embedding Features (REFs).
• To enhance the construction of robust feature representations from a single guidance image.
• To improve contextual understanding in the query image for better segmentation outcomes.

Main Methods:

• Developed Rich Embedding Features (REFs) by combining global, peak, and adaptive embeddings from a reference image.
• Integrated a depth-priority context module to extract contextual cues from the query image.
• Evaluated the approach on Pascal VOC 2012 and COCO datasets.

Main Results:

• The proposed REF approach effectively constructs rich embedding features from a single reference image.
• The combination of REF and the depth-priority context module significantly improved one-shot semantic segmentation performance.
• Experiments demonstrated the approach's effectiveness on benchmark datasets.

Conclusions:

• Rich Embedding Features (REFs) provide a simple yet effective method for one-shot semantic segmentation.
• The proposed method advances the state-of-the-art in segmenting unseen object categories with minimal supervision.
• This work offers a promising direction for efficient and accurate semantic segmentation tasks.