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

TL;DR

This paper introduces a task-driven fixation network that dynamically selects regions of interest for efficient image analysis, reducing computational costs while maintaining high task performance.

Contribution

It proposes a novel neural architecture with automatic fixation point selection, integrating low- and high-resolution features for efficient task-specific image processing.

Findings

Reduces computational overhead compared to exhaustive methods.

Maintains high accuracy with fewer high-resolution analyses.

Demonstrates effectiveness on complex visual tasks.

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.