Task-Driven Fixation Network: An Efficient Architecture with Fixation Selection
This addresses computational bottlenecks for researchers and practitioners in computer vision, though it appears incremental as it builds on existing multi-resolution approaches.
The paper tackles the problem of computational inefficiency in neural networks by proposing a novel architecture with automatic fixation point selection that focuses high-resolution analysis only on regions of interest, achieving comparable task performance with reduced network size and computational overhead.
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.