Visual Gap

The "visual gap" refers to the performance discrepancies between computer vision systems and human perception, particularly when dealing with domain shifts, unseen data, or complex reasoning tasks. Current research focuses on bridging this gap through techniques like contrastive learning, leveraging large language models (LLMs) to process visual information in a more semantically rich way, and developing methods to mitigate hallucinations and biases in vision-language models. Addressing the visual gap is crucial for improving the robustness and reliability of AI systems in real-world applications, ranging from object detection and visual question answering to more complex tasks like visual navigation and abstract visual reasoning.