T-Rex：基于视觉提示的计数
T-Rex: Counting by Visual Prompting
AI总结：
本文提出交互式目标计数模型T-Rex，将计数视为结合视觉提示的开放集检测任务，支持用户通过点或框指定目标并交互优化结果，在多个基准上实现SOTA并展现卓越零样本能力。
摘要：
我们介绍了T-Rex，一个交互式目标计数模型，旨在先检测再计数任意目标。我们将目标计数表述为结合视觉提示的开放集目标检测任务。用户可通过在参考图像上标记点或框来指定感兴趣的目标，T-Rex随后检测所有具有相似模式的目标。在T-Rex视觉反馈的引导下，用户还能通过对遗漏或误检目标进行提示，交互式地优化计数结果。T-Rex在多个类别无关的计数基准上取得了SOTA表现。为进一步挖掘其潜力，我们建立了一个包含多样场景与挑战的新计数基准。定量和定性结果均表明，T-Rex具备卓越的零样本计数能力。我们还展示了T-Rex的多种实际应用场景，体现了其在视觉提示领域的潜力。
英文摘要：
We introduce T-Rex, an interactive object counting model designed to first detect and then count any objects. We formulate object counting as an open-set object detection task with the integration of visual prompts. Users can specify the objects of interest by marking points or boxes on a reference image, and T-Rex then detects all objects with a similar pattern. Guided by the visual feedback from T-Rex, users can also interactively refine the counting results by prompting on missing or falsely-detected objects. T-Rex has achieved state-of-the-art performance on several class-agnostic counting benchmarks. To further exploit its potential, we established a new counting benchmark encompassing diverse scenarios and challenges. Both quantitative and qualitative results show that T-Rex possesses exceptional zero-shot counting capabilities. We also present various practical application scenarios for T-Rex, illustrating its potential in the realm of visual prompting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
