Vision system improves object detection by focusing on key image parts
Task-driven Processing with Coarse-to-Fine Glimpse-based Active Perception
Computer Vision and Pattern Recognition
Summary
When computers look at pictures to find objects, they usually analyze the whole image, which can miss small details or be slow. The authors propose a method that lets the system zoom in step-by-step on the important parts based on the task it needs to do. This targeted approach helps the system find objects more accurately and efficiently, sometimes even outperforming bigger, slower detectors. Their method was tested on high-resolution object detection challenges and showed clear improvements.
What this means in practice
- •For robotics engineers: Equip robots with vision systems that selectively focus on relevant objects in cluttered environments for better real-time detection.
- •For mobile app developers: Create lightweight apps that detect objects accurately in high-resolution images by processing only key parts, reducing resource use.
Authors
Oleh Kolner, Thomas Ortner, Stanisław Woźniak, Angeliki Pantazi
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.