Task-driven Processing with Coarse-to-Fine Glimpse-based Active Perception

2026-09-08

AI Summary

本文针对高分辨率图像中特定目标检测问题，提出了一种从粗到细基于瞥视的任务驱动感知方法（CF-GAP），通过迭代聚焦相关区域提高检测精度。

Abstract

State-of-the-art vision models process images in their entirety, lacking the ability to selectively zoom in on relevant regions. This limitation is particularly acute in scenarios where processing must be conditioned on a specific task - such as instance detection, which requires localizing a specific object in a high-resolution, cluttered scene. In such settings, critical details are easily lost as images are often resized to match the model dimensions and computational constraints. We introduce Coarse-to-Fine Glimpse-based Active Perception (CF-GAP), a task-driven front-end that enhances high-resolution processing of existing instance detectors. CF-GAP selectively directs a sequence of limited view glimpses across the scene, utilizing task information to iteratively refine focus on the most relevant regions. These localized regions are then processed at high resolution by a downstream instance detector. By avoiding full-image processing and eliminating irrelevant confounding information, CF-GAP improves Average Precision (AP) by up to 20% across various state-of-the-art instance detectors on the HR-InsDet and Robotools benchmarks, while further enabling lightweight detectors to outperform their larger counterparts.

Problem

• task-driven processing
• coarse-to-fine
• glimpse-based active perception
• instance detection
• high-resolution

Innovation

• Coarse-to-Fine
• Glimpse-based
• Active Perception
• Task-driven