论文标题
学会计算任何内容：无参考的类不足的计数和微弱的监督
Learning to Count Anything: Reference-less Class-agnostic Counting with Weak Supervision
论文作者
论文摘要
当前的类不足的计数方法可以推广到看不见的类，但通常需要参考图像来定义要计数的对象的类型，以及训练过程中实例注释。无参考的类不足的计数是一个新兴领域，可以将计算为重复识别任务。这种方法有助于计算不变的集合成分。我们表明，具有全局上下文的一般特征空间可以列举图像中的实例，而无需在存在的对象类型上。具体而言，我们证明了视觉变压器功能的回归没有点级监督或参考图像优于其他无参考方法，并且具有使用参考图像的方法竞争。我们在当前的标准数量计数数据集FSC-147上显示这一点。我们还提出了一个改进的数据集FSC-133，该数据集消除了FSC-147中的错误，歧义和重复的图像，并在其上表现出相似的性能。据我们所知，我们是第一个弱监督的无参考阶级计数方法。
Current class-agnostic counting methods can generalise to unseen classes but usually require reference images to define the type of object to be counted, as well as instance annotations during training. Reference-less class-agnostic counting is an emerging field that identifies counting as, at its core, a repetition-recognition task. Such methods facilitate counting on a changing set composition. We show that a general feature space with global context can enumerate instances in an image without a prior on the object type present. Specifically, we demonstrate that regression from vision transformer features without point-level supervision or reference images is superior to other reference-less methods and is competitive with methods that use reference images. We show this on the current standard few-shot counting dataset FSC-147. We also propose an improved dataset, FSC-133, which removes errors, ambiguities, and repeated images from FSC-147 and demonstrate similar performance on it. To the best of our knowledge, we are the first weakly-supervised reference-less class-agnostic counting method.
