Conformer: Local Features Coupling Global Representations for Recognition and Detection.

Area of Science:

• Computer Vision
• Deep Learning
• Artificial Intelligence

Background:

• Convolutional Neural Networks (CNNs) excel at local feature extraction but struggle with global representations.
• Vision transformers capture long-distance dependencies but can lose local feature details.
• A need exists for hybrid models that leverage the strengths of both CNNs and transformers.

Purpose of the Study:

• To propose a novel hybrid network, Conformer, integrating CNN local features and transformer global representations.
• To enhance representation learning by coupling features at different resolutions interactively.
• To develop a Conformer-based object detector (ConformerDet) for improved performance.

Main Methods:

• Conformer employs a dual structure to preserve both local details and global dependencies.
• Feature coupling between CNN local features and transformer global representations occurs at multiple resolutions.
• ConformerDet utilizes region-level feature coupling in an augmented cross-attention mechanism.

Main Results:

• Conformer demonstrates superior performance in visual recognition tasks on the ImageNet dataset.
• Conformer-based object detection (ConformerDet) shows significant improvements on the MS COCO dataset.
• The hybrid approach effectively balances local and global feature learning.

Conclusions:

• Conformer offers enhanced representation learning by synergizing convolution and self-attention.
• The proposed model shows strong potential as a general-purpose backbone network for computer vision.
• Conformer provides a robust framework for both visual recognition and object detection.