Vision Backbone

A vision backbone is the foundational feature extraction component of many computer vision models, aiming to efficiently and effectively represent image data. Current research emphasizes developing more computationally efficient backbones, exploring architectures that combine convolutional neural networks and transformers, or that replace computationally expensive attention mechanisms with alternatives like Fourier filtering or recurrent structures. These efforts aim to improve the speed and accuracy of vision models across various tasks, from image classification and object detection to semantic segmentation, particularly for resource-constrained environments like mobile devices and edge computing. The resulting improvements in efficiency and performance have significant implications for both advancing the field and enabling broader real-world applications.