18422887. OPEN-VOCABULARY OBJECT DETECTION IN IMAGES simplified abstract (Google LLC)

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 18422887 titled 'OPEN-VOCABULARY OBJECT DETECTION IN IMAGES

Simplified Explanation

The abstract describes a method for object detection using a neural network that processes images and query embeddings to generate object detection data.

• 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 driving, surveillance systems, robotics, and image recognition software.

Problems Solved

This technology solves the problem of accurately detecting and classifying objects in images, which is essential for tasks like object tracking, scene understanding, and visual search.

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

• "Enhancing Object Detection in Autonomous Vehicles Using Neural Networks"

Possible Prior Art

One possible prior art could be the use of traditional computer vision algorithms for object detection before the advent of neural networks.

Unanswered Questions

How does this method compare to other object detection techniques in terms of accuracy and efficiency?

The article does not provide a direct comparison with other object detection techniques, so it is unclear how this method performs in relation to existing methods.

Are there any limitations or constraints to the scalability of this technology for large-scale applications?

The scalability of this technology for processing a large number of images or complex scenes is not discussed in the article, leaving room for further exploration on potential limitations.

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.