MS-Glance: Bio-Inspired Non-Semantic Context Vectors and their Applications in Supervising Image Reconstruction

Ziqi Gao, Wendi Yang, Yujia Li, Lei Xing, S. Kevin Zhou; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 3084-3095

Non-semantic context information is crucial for visual recognition as the human visual perception system first uses global statistics to process scenes rapidly before identifying specific objects. However while semantic information is increasingly incorporated into computer vision tasks such as image reconstruction non-semantic information such as global spatial structures is often overlooked. To bridge the gap we propose a biologically informed non-semantic context descriptor MS-Glance along with the Glance Index Measure for comparing two images. A Global Glance vector is formulated by randomly retrieving pixels based on a perception-driven rule from an image to form a vector representing non-semantic global context while a local Glance vector is a flattened local image window mimicking a zoom-in observation. The Glance Index is defined as the inner product of two standardized sets of Glance vectors. We evaluate the effectiveness of incorporating Glance supervision in two reconstruction tasks: image fitting with implicit neural representation (INR) and undersampled MRI reconstruction. Extensive experimental results show that MS-Glance outperforms existing image restoration losses across both natural and medical images. The code is available at https://github.com/Z7Gao/MSGlance.