文本可提示的物体计数：通过数量感知增强
Text-promptable Object Counting via Quantity Awareness Enhancement
AI总结：
提出QUANet，通过数量导向文本提示和双流解码器增强数量感知，实现零样本类别无关物体计数，在多个基准上表现优异。
摘要：
大型视觉-语言模型（VLMs）的最新进展在解决文本可提示的物体计数问题上取得了显著进步。代表性方法通常使用包含图像中物体类别信息的文本提示。然而，这对于训练模型在计数任务中准确区分物体数量是不够的。为此，我们提出了QUANet，引入了新颖的面向数量的文本提示以及视觉-文本数量对齐损失，以增强模型的数量感知能力。此外，我们提出了一种双流自适应计数解码器，包括Transformer流、CNN流以及若干Transformer到CNN增强适配器（T2C-适配器），用于密度图预测。T2C-适配器促进了Transformer和CNN流之间有效的知识沟通与聚合。最后，我们提出了一种跨流数量排序损失，以优化来自两个流的预测的排序顺序。在标准基准如FSC-147、CARPK、PUCPR+和上海Tech上的大量实验表明，我们的模型在零样本类别无关计数方面具有强大的泛化能力。代码可在https://github.com/viscom-tongji/QUANet获取。
英文摘要：
Recent advances in large vision-language models (VLMs) have shown remarkable progress in solving the text-promptable object counting problem. Representative methods typically specify text prompts with object category information in images. This however is insufficient for training the model to accurately distinguish the number of objects in the counting task. To this end, we propose QUANet, which introduces novel quantity-oriented text prompts with a vision-text quantity alignment loss to enhance the model's quantity awareness. Moreover, we propose a dual-stream adaptive counting decoder consisting of a Transformer stream, a CNN stream, and a number of Transformer-to-CNN enhancement adapters (T2C-adapters) for density map prediction. The T2C-adapters facilitate the effective knowledge communication and aggregation between the Transformer and CNN streams. A cross-stream quantity ranking loss is proposed in the end to optimize the ranking orders of predictions from the two streams. Extensive experiments on standard benchmarks such as FSC-147, CARPK, PUCPR+, and ShanghaiTech demonstrate our model's strong generalizability for zero-shot class-agnostic counting. Code is available at https://github.com/viscom-tongji/QUANet
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。
