Systems and methods for attention-based configurable convolutional neural networks (ABC-CNN) for visual question answering
Described herein are systems and methods for generating and using attention-based deep learning architectures for visual question answering task (VQA) to automatically generate answers for image-related (still or video i…
Original patent title: “Systems and methods for attention-based configurable convolutional neural networks (ABC-CNN) for visual question answering”
What this patent covers
The actual claim
Described herein are systems and methods for generating and using attention-based deep learning architectures for visual question answering task (VQA) to automatically generate answers for image-related (still or video images) questions. To generate the correct answers, it is important for a model's attention to focus on the relevant regions of an image according to the question because different questions may ask about the attributes of different image regions. In embodiments, such question-guided attention is learned with a configurable convolutional neural network (ABC-CNN). Embodiments of the ABC-CNN models determine the attention maps by convolving image feature map with the configurable convolutional kernels determined by the questions semantics. In embodiments, the question-guided attention maps focus on the question-related regions and filters out noise in the unrelated regions.
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Patent Abstract
Patent abstract
Described herein are systems and methods for generating and using attention-based deep learning architectures for visual question answering task (VQA) to automatically generate answers for image-related (still or video images) questions. To generate the correct answers, it is important for a model's attention to focus on the relevant regions of an image according to the question because different questions may ask about the attributes of different image regions. In embodiments, such question-guided attention is learned with a configurable convolutional neural network (ABC-CNN). Embodiments of the ABC-CNN models determine the attention maps by convolving image feature map with the configurable convolutional kernels determined by the questions semantics. In embodiments, the question-guided attention maps focus on the question-related regions and filters out noise in the unrelated regions.
Patent Journey
From filing to today
Patent Filed
2016
Patent Granted
2018 · 2yr after filing
Active Today
2026
Expires
2036
PatentBrief Score
Impact Score
Moderate
Citation count
29/40
Moderately cited
Claim breadth
15/20
Broad claims
Recency
10/20
Granted 5–10 years ago
Assignee scale
0/20
Independent or smaller assignee
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
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