groundingLMM

GLaMM (Grounding Large Multimodal Model) is an end-to-end trained LMM capable of generating natural language responses integrated with object segmentation masks, enabling visual grounding and versatile interaction with images at multiple granularity levels. It introduces the novel task of Grounded Conversation Generation (GCG), supports various downstream applications like referring expression segmentation and region-level captioning, and is underpinned by the large-scale GranD dataset.

groundingLMM sits in the Vision / Multimodal category, so it is usually better judged by workflow fit, deployment style, and team needs than by isolated features alone. Based on the available metadata, it leans most clearly toward Generates natural language responses seamlessly integrated with object segmentation masks.. A representative use case is Interactive visual assistants that understand and respond to user queries about specific image regions.. The listed license is Apache-2.0. GitHub stars are currently 969. Relevant tags include Multimodal AI, Computer Vision.