DAVE——一种用于低样本计数的检测与验证范式
DAVE -- A Detect-and-Verify Paradigm for Low-Shot Counting
AI总结：
提出基于检测与验证范式的低样本计数器DAVE，通过生成高召回检测集并验证移除异常值，同时提升召回率与精确率，在总计数MAE和检测质量上均优于现有方法并达到新SOTA。
摘要：
低样本计数器根据图像中仅标注的少量或零样本，估计对应选定类别的物体数量。当前最先进的方法将总计数估计为物体位置密度图的求和，但未提供单个物体的位置和大小，而这对于许多应用至关重要。基于检测的计数器解决了这一问题，但在总计数精度上却落后。此外，由于存在大量误报，这两种方法在其他物体类别存在时往往会高估计数。我们提出了 DAVE，一种基于检测与验证范式的低样本计数器，它通过首先生成高召回率的检测集，然后验证检测结果以识别并移除异常值，从而避免了上述问题。这同时提高了召回率和精确率，从而实现了准确的计数。DAVE 在总计数 MAE 上比顶级基于密度的计数器高出约 20%，在检测质量上比最新的基于检测的计数器高出约 20%，并在零样本及基于文本提示的计数中设定了新的最先进水平。
英文摘要：
Low-shot counters estimate the number of objects corresponding to a selected category, based on only few or no exemplars annotated in the image. The current state-of-the-art estimates the total counts as the sum over the object location density map, but does not provide individual object locations and sizes, which are crucial for many applications. This is addressed by detection-based counters, which, however fall behind in the total count accuracy. Furthermore, both approaches tend to overestimate the counts in the presence of other object classes due to many false positives. We propose DAVE, a low-shot counter based on a detect-and-verify paradigm, that avoids the aforementioned issues by first generating a high-recall detection set and then verifying the detections to identify and remove the outliers. This jointly increases the recall and precision, leading to accurate counts. DAVE outperforms the top density-based counters by ~20% in the total count MAE, it outperforms the most recent detection-based counter by ~20% in detection quality and sets a new state-of-the-art in zero-shot as well as text-prompt-based counting.
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。
