openpsg
Maintainer: cjwbw
1
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | View on Github |
| Paper link | View on Arxiv |
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Model overview
openpsg is a powerful AI model for Panoptic Scene Graph Generation (PSG). Developed by researchers at Nanyang Technological University and SenseTime Research, openpsg aims to provide a comprehensive scene understanding by generating a scene graph representation that is grounded by pixel-accurate segmentation masks. This contrasts with classic Scene Graph Generation (SGG) datasets that use bounding boxes, which can result in coarse localization, inability to ground backgrounds, and trivial relationships.
The openpsg model addresses these issues by using the COCO panoptic segmentation dataset to annotate relations based on segmentation masks rather than bounding boxes. It also carefully defines 56 predicates to avoid trivial or duplicated relationships. Similar models like gfpgan for face restoration, segmind-vega for accelerated Stable Diffusion, stable-diffusion for text-to-image generation, cogvlm for powerful visual language modeling, and real-esrgan for blind super-resolution, also tackle complex visual understanding tasks.
Model inputs and outputs
The openpsg model takes an input image and generates a scene graph representation of the content in the image. The scene graph consists of a set of nodes (objects) and edges (relationships) that comprehensively describe the scene.
Inputs
- Image: The input image to be analyzed.
- Num Rel: The desired number of relationships to be generated in the scene graph, ranging from 1 to 20.
Outputs
- Scene Graph: An array of scene graph elements, where each element represents a relationship in the form of a subject, predicate, and object, all grounded by their corresponding segmentation masks in the input image.
Capabilities
openpsg excels at holistically understanding complex scenes by generating a detailed scene graph representation. Unlike classic SGG approaches that focus on objects and their relationships, openpsg considers both "things" (objects) and "stuff" (backgrounds) to provide a more comprehensive interpretation of the scene.
What can I use it for?
The openpsg model can be useful for a variety of applications that require a deep understanding of visual scenes, such as:
- Robotic Vision: Enabling robots to better comprehend their surroundings and interact with objects and environments.
- Autonomous Driving: Improving scene understanding for self-driving cars to navigate more safely and effectively.
- Visual Question Answering: Enhancing the ability to answer questions about the contents and relationships in an image.
- Image Captioning: Generating detailed captions that describe not just the objects, but also the interactions and spatial relationships in a scene.
Things to try
With the openpsg model, you can experiment with various types of images to see how it generates the scene graph representation. Try uploading photos of everyday scenes, like a living room or a park, and observe how the model identifies the objects, their attributes, and the relationships between them. You can also explore the potential of using the scene graph output for downstream tasks like visual reasoning or image-text matching.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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