Adobe, Inc.

Baidu USA LLC

Kan Chen

Zhaowen Wang

Trung Huu Bui

Jiang Wang

Chen Fang

Wei Xu

Methods and systems are provided for ranking images against queries. A visual modality ranking of visual features of a digital image against a query is generated. A language modality ranking of text features of text associated with the digital image against the query is also generated. A multi-modal neural network determines importance weightings of the language modality ranking and the visual modality ranking against the query. The visual modality ranking and the language modality ranking are combined into a multi-modal ranking of the digital image against the query based on the importance weightings. The digital image is provided as a search result of the query based on the multi-modal ranking.

Multi-modal image ranking using neural networks

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.

Systems and methods for attention-based configurable convolutional neural networks (ABC-CNN) for visual question answering

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Los Angeles, CA, United States of America

Kan Chen