Enhancing Semantic Segmentation with CLIP: Leveraging Cross-Modal Understanding for Image Analysis

Ziyi Han

Abstract: Image semantic segmentation, although not a new concept, has found significant application in various domains. For instance, it is widely used in autonomous driving for scene understanding and obstacle detection, in medical imaging for organ segmentation and anomaly detection, and in satellite imagery for land cover classification and urban planning. Despite numerous research efforts to improve image semantic segmentation, challenges such as fine-grained object delineation, handling complex scenes with multiple overlapping objects, and achieving robustness to diverse environmental conditions persist. To address these challenges, we propose leveraging the CLIP (Contrastive Language-Image Pretraining) framework for image semantic segmentation. CLIP, a recent breakthrough in computer vision and natural language processing, learns visual representations by jointly training on large-scale image-text pairs. By fine-tuning CLIP on image semantic segmentation tasks, we aim to leverage its ability to understand the semantic context of images and improve the accuracy and generalization of segmentation models. Through this approach, we anticipate overcoming some of the limitations of traditional segmentation methods and achieving more robust and effective semantic segmentation results across various applications.

Keywords: Semantic Segmentation CLIP Transformer