Google llc (20240161459). OPEN-VOCABULARY OBJECT DETECTION IN IMAGES simplified abstract

OPEN-VOCABULARY OBJECT DETECTION IN IMAGES

Organization Name

google llc

Inventor(s)

Matthias Johannes Lorenz Minderer of Zurich (CH)

Alexey Alexeevich Gritsenko of Amsterdam (NL)

Austin Charles Stone of San Francisco CA (US)

Dirk Weissenborn of Berlin (DE)

Alexey Dosovitskiy of Berlin (DE)

Neil Matthew Tinmouth Houlsby of Zürich (CH)

OPEN-VOCABULARY OBJECT DETECTION IN IMAGES - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240161459 titled 'OPEN-VOCABULARY OBJECT DETECTION IN IMAGES

Simplified Explanation

The patent application describes a method for object detection using a neural network.

• Obtaining an image and a set of query embeddings representing object categories.
• Processing the image with an image encoding subnetwork to generate object embeddings.
• Processing each object embedding with a localization subnetwork to define a region in the image.
• Processing object and query embeddings with a classification subnetwork to generate classification scores.

Potential Applications

This technology can be applied in various fields such as autonomous vehicles, surveillance systems, and robotics for object detection and recognition.

Problems Solved

This technology solves the problem of accurately detecting and classifying objects in images, which is essential for many computer vision applications.

Benefits

The benefits of this technology include improved accuracy in object detection, faster processing speeds, and the ability to handle a wide range of object categories.

Potential Commercial Applications

The potential commercial applications of this technology include security systems, retail analytics, and industrial automation for efficient object detection and classification.

Possible Prior Art

One possible prior art for this technology could be the use of convolutional neural networks for object detection and classification in images.

Unanswered Questions

How does this method compare to traditional object detection techniques?

The article does not provide a direct comparison between this method and traditional object detection techniques. It would be interesting to see a performance comparison in terms of accuracy and speed.

What are the limitations of this method in terms of scalability to a large number of object categories?

The article does not address the scalability of this method to a large number of object categories. It would be important to understand how well the system performs when dealing with a wide variety of objects.

Original Abstract Submitted

methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for object detection. in one aspect, a method comprises: obtaining: (i) an image, and (ii) a set of one or more query embeddings, wherein each query embedding represents a respective category of object; processing the image and the set of query embeddings using an object detection neural network to generate object detection data for the image, comprising: processing the image using an image encoding subnetwork of the object detection neural network to generate a set of object embeddings; processing each object embedding using a localization subnetwork to generate localization data defining a corresponding region of the image; and processing: (i) the set of object embeddings, and (ii) the set of query embeddings, using a classification subnetwork to generate, for each object embedding, a respective classification score distribution over the set of query embeddings.