How do different architectural approaches for combining visual and linguistic features in multimodal models af

Abstract

Abstract: A convolutional neural network (CNN) is one of the most significant networks in the deep learning field. Since CNN made impressive achievements in many areas, including but not limited to computer vision and natural language processing, it attracted much attention from both industry and academia in the past few years. The existing reviews mainly focus on CNN's applications in different scenarios without considering CNN from a general perspective, and some novel ideas proposed recently are not covered. In this review, we aim to provide some novel ideas and prospects in this fast-growing field.

Research Question

How do different architectural approaches for combining visual and linguistic features in multimodal models affect inference efficiency on Visual Genome tasks, measured by throughput (samples/second) and latency across GPU hardware platforms while maintaining competitive accuracy on scene understanding benchmarks?

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