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arXiv · 2501.01548

Task-Driven Fixation Network: An Efficient Architecture with Fixation Selection

Shuguang Wang·Yuanjing Wang
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Abstract

This paper presents a novel neural network architecture featuring automatic fixation point selection, designed to efficiently address complex tasks with reduced network size and computational overhead. The proposed model consists of: a low-resolution channel that captures low-resolution global features from input images; a high-resolution channel that sequentially extracts localized high-resolution features; and a hybrid encoding module that integrates the features from both channels. A defining characteristic of the hybrid encoding module is the inclusion of a fixation point generator, which dynamically produces fixation points, enabling the high-resolution channel to focus on regions of interest. The fixation points are generated in a task-driven manner, enabling the automatic selection of regions of interest. This approach avoids exhaustive high-resolution analysis of the entire image, maintaining task performance and computational efficiency.

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Shuguang Wang, Yuanjing Wang. 2025-01-02. Task-Driven Fixation Network: An Efficient Architecture with Fixation Selection. https://arxiv.org/abs/2501.01548

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