Visual Semantics

Visual semantics research focuses on understanding how computers can interpret the meaning of visual information, bridging the gap between visual perception and linguistic understanding. Current efforts concentrate on improving multimodal models, particularly Vision-Language Models (VLMs), using techniques like contrastive learning, attention mechanisms, and incorporating symbolic reasoning to better capture complex relationships within and between visual and textual data. This field is crucial for advancing applications such as scene understanding, multimodal sarcasm detection, and improving the safety and reliability of AI systems, particularly in areas like medical image analysis and autonomous driving. Furthermore, research is actively exploring ways to align visual representations learned by artificial neural networks with those observed in human brain activity.