This AI Paper Introduces Grounding Large Multimodal Model (GLaMM): An End-to-End Trained Large Multimodal Model that Provides Visual Grounding Capabilities with the Flexibility to Process both Image and Region Inputs

Grounding Large Multimodal Model (GLaMM) is introduced as a novel model for visually grounded conversations. GLaMM allows for natural language replies combined with object segmentation masks, providing improved user engagement. The researchers also introduce the Grounded Conversation Generation (GCG) task and the Grounding-anything Dataset (GranD) to aid in model training and evaluation.

Introducing GLaMM: An AI Model for Visual Grounding

Large Multimodal Models (LMMs) are playing a crucial role in bridging the gap between language and visual tasks. Models like LLaVa, miniGPT4, Otter, InstructBLIP, LLaMA-Adapter v2, and mPLUGOWL are early versions that provide efficient textual answers based on input photos. However, these models need to anchor their decisions on the visual environment. To overcome this limitation, researchers have developed GLaMM, an end-to-end trained model that combines in-depth region awareness, pixel-level groundings, and conversational abilities.

How GLaMM Works

GLaMM generates natural language replies rooted at the pixel level in the input image. It represents various levels of granularity, including things, stuff, and object parts. This multimodal conversational model can produce precise pixel-level groundings and engage in visually grounded conversations.

Addressing the Lack of Standards

The researchers introduce a new task called Grounded Conversation Generation (GCG) to fill the gap in visually grounded dialogues. GCG combines various computer vision tasks, such as phrase grounding, captioning, and expression segmentation. GLaMM, along with the suggested pretraining dataset, can be used for conversational-style QA, region-level captioning, picture captioning, and expression segmentation.

The GranD Dataset

To aid in model training and assessment, the researchers have developed the Grounding-anything Dataset (GranD). It is a densely annotated dataset with 7.5 million distinct ideas based on 810 million locations. GranD includes 11 million photos, 33 million grounded captions, and 84 million reference terms. The dataset was created using an automated annotation pipeline and verification processes.

Benefits and Applications

GLaMM provides a unique user experience by combining textual and visual suggestions. It can be used for various applications, such as interactive embodied agents, localized content alteration, and deep visual understanding. The model’s flexibility to process both image and region inputs makes it valuable for middle managers looking to leverage AI solutions.

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