核心概念
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.
要約
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.
統計
"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."
引用
"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."