2 Dimensional Vision Language Model

Two-dimensional vision-language models (VLMs) aim to bridge the gap between visual and textual information, enabling computers to understand and generate descriptions of images. Current research focuses on leveraging these 2D VLMs to improve 3D scene understanding, often through techniques like feature distillation and cross-modal self-training, with architectures such as CLIP playing a prominent role. This research is significant because it advances the capabilities of AI systems to interact with the physical world through language, impacting applications in robotics, 3D scene segmentation, and other areas requiring multimodal understanding.