Vision Language Fusion

Vision-language fusion aims to integrate visual and textual information for improved understanding and reasoning in computer vision tasks. Current research heavily utilizes transformer-based architectures, often incorporating pre-trained vision-language models like CLIP and exploring techniques such as prompt tuning and early/late fusion strategies to effectively combine image and text features. This field is driving advancements in various applications, including object detection (especially open-vocabulary and aerial), visual grounding, and fine-grained image classification, by enabling more robust and context-aware systems. The resulting models demonstrate improved accuracy and efficiency compared to purely vision-based approaches.