We propose FindIt, a simple and versatile framework that unifies a variety of visual grounding and localization tasks including referring expression comprehension, text-based localization, and object detection. Key to our architecture is an efficient multi-scale fusion module that unifies the disparate localization requirements across the tasks. In addition, we discover that a standard object detector is surprisingly effective in unifying these tasks without a need for task-specific design, losses, or pre-computed detections. Our end-to-end trainable framework responds flexibly and accurately to a wide range of referring expression, localization or detection queries for zero, one, or multiple objects. Jointly trained on these tasks, FindIt outperforms the state of the art on both referring expression and text-based localization, and shows competitive performance on object detection. Finally, FindIt generalizes better to out-of-distribution data and novel categories compared to strong single-task baselines. All of these are accomplished by a single, unified and efficient model. The code will be released.

FindIt是一个简单而多功能的框架，有效地统一了各种视觉相关任务，包括基于文本的定位、物体检测和短语理解任务。该框架关键在于实现了一个高效的多尺度融合模块，统一了这些任务的不同需求，并发现使用标准目标检测器可以在不需要特定的设计、损失或先前计算出的结果的情况下，有效地统一这些任务。在多个任务上训练的FindIt框架在短语理解和基于文本的定位方面表现更好，而在物体检测方面表现出竞争性。此外，FindIt相比于单任务的基本线性更好地推广到数据和新类别上。

FindIt：自然语言查询下的通用定位