Ferret Introduction
Ferret is a new Multimodal Large Language Model (MLLM) designed to understand spatial referring of any shape or granularity within an image and accurately ground open-vocabulary descriptions. This innovative model employs a novel hybrid region representation that integrates discrete coordinates and continuous features to represent a region in the image.
Ferret Features
Novel Hybrid Region Representation
Ferret utilizes a novel and powerful hybrid region representation that jointly represents a region in the image by integrating discrete coordinates and continuous features. This allows Ferret to accurately understand and ground any shape or granularity within an image.
Spatial-Aware Visual Sampler
To extract continuous features of versatile regions, Ferret proposes a spatial-aware visual sampler. This sampler is adept at handling varying sparsity across different shapes, enabling Ferret to accept diverse region inputs such as points, bounding boxes, and free-form shapes.
Comprehensive Instruction Tuning Dataset: GRIT
To bolster Ferret's capabilities, a comprehensive refer-and-ground instruction tuning dataset named GRIT is curated. This dataset includes 1.1M samples that contain rich hierarchical spatial knowledge, with 95K hard negative data to promote model robustness.
Superior Performance
Ferret achieves superior performance in classical referring and grounding tasks and greatly outperforms existing MLLMs in region-based and localization-demanded multimodal chatting. Additionally, Ferret shows significantly improved capability in describing image details and a remarkable alleviation in object hallucination.
Ferret Application Scenarios
Image Understanding
Ferret can be applied in image understanding tasks that require accurate spatial referring and grounding, such as object detection, image segmentation, and visual question answering.
Multimodal Chatting
With its powerful referring and grounding capabilities, Ferret can significantly enhance the quality of multimodal chatting by providing more accurate and detailed image descriptions.
Ferret Technical Details
Model Architecture
Ferret's architecture is based on the Transformer model, which is known for its excellent performance in natural language processing tasks. The model is pre-trained on large-scale multimodal data and fine-tuned on the GRIT dataset.
Training and Inference
Ferret is trained using the Adam optimizer and employs techniques such as layer normalization and dropout to improve model performance. During inference, Ferret uses beam search to generate the most likely referring and grounding results.
Ferret FAQs
Q: What is the difference between Ferret and other MLLMs?
A: Ferret stands out from other MLLMs with its novel hybrid region representation and spatial-aware visual sampler, enabling it to handle diverse region inputs and achieve superior referring and grounding performance.
Q: How can I get access to the Ferret model and dataset?
A: The code and data for Ferret will be available at the project website mentioned in the paper. You can visit the website to download the resources and experiment with the model.
Q: What are the future research directions for Ferret?
A: Future research directions for Ferret include extending its capabilities to video understanding, enhancing its performance in real-world scenarios, and exploring new applications in various fields such as robotics and virtual reality.
