As Diffusion Model and related customization (Personalization) work become more and more popular, such as DreamBooth, Textual Inversion, Custom Diffusion, etc., this type of method can extract the concept of a specific object from the picture and add it to the preset In the trained Text-to-Image Diffusion Model, people can customize the generation of objects they are interested in, such as specific anime characters or home sculptures, water cups, etc.
These customization methods mainly focus on capturing the appearance of objects. However, in addition to the appearance of objects, there is another important pillar in the visual world, which is the inextricable relationship between objects. At present, no work has explored how to extract a specific relationship (Relation) from the image and apply this relationship to the generation task.
So why are relationships between objects so important? Because these relationships can help us better understand and describe objects, allowing our generative models to more accurately capture the real situation of the scene. For example, in one photo we can see a person standing next to a car. If we only focus on the respective appearances of people and cars, we might get a visually accurate but semantically inaccurate description, i.e. "There is a person and a car in the photo". However, if we can capture the relationship between the person and the car, we can get a more accurate and semantically meaningful description, such as “this person is driving this car” or “this person is taking a photo with this car”.
There has been some work exploring how to extract relationships between objects from images. For example, some researchers use Graph Neural Networks to learn relationships between objects in images. They proposed a model called "RelationNet" that can encode the relationships between objects into vectors and use these vectors in generative tasks. In this model, objects are represented as nodes, and relationships between them are represented as edges. This model can embed objects and relationships into a unified vector space, so that the relationships between objects can be directly used in generation tasks.
In addition to using graph neural networks, there are some other methods that can be used to extract relationships between objects. For example, some researchers use Self-Attention Mechanism to learn the relationships between objects in images. In this method, the model can automatically focus on the interactions between different objects, thereby learning the relationships between objects.
In addition to learning the relationships between objects, there are other tasks that can help us better understand the scene. For example, in generation tasks, we can use semantic segmentation to distinguish different objects and embed them into a unified vector space. This way, we can more accurately capture the relationships between different objects.
In summary, the relationships between objects are very important for both scene understanding and generation tasks. There has been some work exploring how to extract relationships between objects from images and use them in generative tasks. In the future, we can look forward to more work to further explore this issue and apply it to practical scenarios.