Abstract

Top-down visual attention models are essential for task-driven object detection; however, many existing approaches do not effectively integrate multiple sources of high-level guidance such as scene context and target-specific information. This paper proposes a computational framework that combines contextual information and target object features to dynamically modulate low-level visual features for improved attentional selection. The proposed model consists of three key components: (i) a contextual weighting module that learns feature weights optimised using Particle Swarm Optimisation and predicted through a hetero-associative neural network; (ii) a target-aware attention module that estimates feature importance based on target-specific characteristics derived from low-level feature distributions; and (iii) a recognition module that performs region classification using a Naïve Bayes classifier. Experiments conducted on seven challenging datasets with diverse objects and cluttered backgrounds demonstrate that integrating contextual and target-specific information improves detection performance compared to using either source independently. The primary objective of this work is to investigate the complementary and synergistic effects of combining contextual information and target object knowledge within a bottom-up saliency framework. The goal is not to propose or claim a state-of-the-art top-down visual attention model, but rather to demonstrate that integrating multiple sources of guidance can enhance saliency-based target detection under varied visual conditions.

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