Efficient Semantic Segmentation with Zero-Shot and Open-Vocabulary Models

In this article, we present a novel approach to semantic segmentation called SCLIP (Scalable Context-aware Language-informed Segmentation), which leverages both local visual features and overarching semantic context to improve the accuracy of semantic segmentation in various challenges. Unlike traditional methods that solely rely on local features, SCLIP incorporates a correlative self-attention mechanism to consider the relationship between local features and semantic contexts, leading to more accurate segmentation results across diverse scales.
We evaluate SCLIP on eight benchmark datasets and show that it outperforms existing state-of-the-art methods, achieving an average mIoU of 38.2%. Our qualitative results demonstrate the effectiveness of SCLIP in segmenting images with clear and accurate masks, especially for high-resolution inputs.
Our approach is designed to overcome the limitations of traditional methods that focus solely on local features. By incorporating a correlative self-attention mechanism, SCLIP can capture the relationships between different parts of an image and better understand the context in which each part appears. This allows SCLIP to produce more accurate segmentation results, especially for challenging scenarios where local features may not be sufficient on their own.
In summary, SCLIP is a powerful approach to semantic segmentation that leverages both local visual features and overarching semantic context to improve accuracy across diverse scales. Its ability to capture relationships between different parts of an image makes it particularly effective in challenging scenarios, and we believe it has the potential to significantly improve the state-of-the-art in this field.