Advancements in Image Interpretation: Self-Supervised Learning of Object Structures for Enhanced Classification and Segmentation

Recent studies indicate that traditional classification models often emphasize the most distinctive parts of an object, overlooking its overall structure. To address this, the look-into-object (LIO) framework was developed, but it relies on supervised learning with labeled data for initial image representation, requiring pretraining with annotated datasets. We propose an enhancement to the LIO framework by replacing its supervised learning classifier with a self-supervised learning (SSL) strategy, enabling the framework to learn comprehensive object structures without labeled data. Additionally, we introduce a warm-up strategy to reduce reliance on pretrained models. Experimental results show that our enhanced model consistently focuses on the entire object, improving classification accuracy (Acc) by integrating object structure knowledge. This approach is adaptable across various computer vision applications, including semantic segmentation, demonstrating its broad applicability. GitHub link: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/maikurufeza/Self-supervised-with-object-structure-information</uri>.