Visual Language Alignment

Visual language alignment (VLA) focuses on bridging the semantic gap between visual and textual data, aiming to enable computers to understand and reason about the relationship between images and descriptions. Current research emphasizes efficient methods for aligning these modalities, exploring techniques like parameter-efficient fine-tuning of pre-trained models (e.g., CLIP, DINOv2, Llama 2) and incorporating richer semantic information such as object attributes and part-level features. These advancements improve performance in tasks like referring expression comprehension, visual question answering, and person re-identification, leading to more robust and interpretable multimodal systems with potential applications in various fields.