任务驱动的基于由粗到细瞥视的主动感知处理

中文摘要 AI 辅助

最先进的视觉模型整体处理图像，缺乏选择性地放大相关区域的能力。这种局限性在必须根据特定任务进行处理的场景中尤为突出——例如实例检测，这需要在高分辨率、杂乱场景中定位特定对象。在此类设置中，由于图像通常被调整大小以匹配模型尺寸和计算约束，关键细节容易丢失。我们提出了由粗到细的基于瞥视的主动感知（CF-GAP），这是一种任务驱动的前端，可增强现有实例检测器的高分辨率处理能力。CF-GAP选择性地引导一系列有限的视野瞥视穿越场景，利用任务信息迭代地精炼对最相关区域的聚焦。这些局部区域随后由下游实例检测器以高分辨率处理。通过避免全图像处理并消除无关的干扰信息，CF-GAP在HR-InsDet和Robotools基准测试中，将各种最先进的实例检测器的平均精度（AP）提高了高达20%，同时使轻量级检测器能够超越其更大的对应版本。

英文摘要

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.

发表机构

• IBM Research（IBM研究院）
• Graz University of Technology（格拉茨工业大学）

机构由 AI 辅助整理，请以论文原文为准。