Core Concepts
ViP-LLaVA, a large multimodal model, can effectively process and understand arbitrary visual prompts overlaid on images, enabling intuitive user interactions and state-of-the-art performance on region-specific visual reasoning tasks.
Abstract
The paper introduces ViP-LLaVA, a novel multimodal model that can process and understand arbitrary visual prompts overlaid on images, such as bounding boxes, arrows, and scribbles. This allows users to interact with the model in a more natural and intuitive way, by directly marking up images with visual cues.
Key highlights:
ViP-LLaVA leverages CLIP's ability to recognize diverse visual markers, and directly overlays these prompts onto the original image, without the need for complex region encoding modules.
This simple yet effective approach outperforms specialized region-encoding models on region-specific tasks like Visual7W, PointQA, and Visual Commonsense Reasoning.
The authors introduce ViP-Bench, a comprehensive benchmark to evaluate multimodal models' capabilities in understanding visual prompts across multiple dimensions, including recognition, OCR, knowledge, math, relationship reasoning, and language generation.
Experiments show ViP-LLaVA outperforms other state-of-the-art multimodal models on ViP-Bench, demonstrating its strong region-level understanding abilities.
The paper also provides in-depth analysis on ViP-LLaVA's performance, including its ability to handle multi-region reasoning, arrow direction understanding, and generalization to unseen visual prompt attributes.
Stats
"The person marked with the red arrow is holding a green flag."
"The stuff within the circle is the liquid from Object 1, which is water."
Quotes
"To address this challenge, we introduce a novel multimodal model capable of decoding arbitrary (free-form) visual prompts. This allows users to intuitively mark images and interact with the model using natural cues like a 'red bounding box' or 'pointed arrow'."
"Our simple design directly overlays visual markers onto the RGB image, eliminating the need for complex region encodings, yet achieves state-of-the-art performance on region-understanding tasks like Visual7W, PointQA, and Visual Commonsense Reasoning benchmark."