Contextual Transformer Networks for Visual Recognition

Transformer with self-attention has led to the revolutionizing of natural language processing field, and recently inspires the emergence of Transformer-style architecture design with competitive results in numerous computer vision tasks. Through extensive experiments over a wide range of applications (e.g., image recognition, object detection, instance segmentation, and semantic segmentation), we validate the superiority of CoTNet as a stronger backbone.

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• 1 Nevertheless, most of existing designs directly employ self-attention over a 2D feature map to obtain the attention matrix based on pairs of isolated queries and keys at each spatial location, but leave the rich contexts among neighbor keys under-exploited.
• 2 In this work, we design a novel Transformer-style module, i.e., Contextual Transformer (CoT) block, for visual recognition.
• 3 Such design fully capitalizes on the contextual information among input keys to guide the learning of dynamic attention matrix and thus strengthens the capacity of visual representation.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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