论文标题
令人沮丧的简单几声对象检测
Frustratingly Simple Few-Shot Object Detection
论文作者
论文摘要
从几个示例中检测稀有物体是一个新的问题。先前的工作表明，元学习是一种有前途的方法。但是，微调技术引起了很少的关注。我们发现，仅在稀有类上进行微调现有检测器的最后一层对于少数拍摄对象检测任务至关重要。这种简单的方法的表现优于元学习方法，在当前基准测试上约为2〜20点，有时甚至使先前方法的准确性翻了一番。但是，少数样本的较高差异通常会导致现有基准的不可靠性。我们通过对多个培训示例进行采样以获得稳定的比较并根据三个数据集建立新的基准来修改评估方案：Pascal VOC，Coco和LVIS。同样，我们的微调方法在修订的基准上建立了新的最新状态。代码以及预验证的型号可在https://github.com/ucbdrive/few-shot-object-detection上找到。
Detecting rare objects from a few examples is an emerging problem. Prior works show meta-learning is a promising approach. But, fine-tuning techniques have drawn scant attention. We find that fine-tuning only the last layer of existing detectors on rare classes is crucial to the few-shot object detection task. Such a simple approach outperforms the meta-learning methods by roughly 2~20 points on current benchmarks and sometimes even doubles the accuracy of the prior methods. However, the high variance in the few samples often leads to the unreliability of existing benchmarks. We revise the evaluation protocols by sampling multiple groups of training examples to obtain stable comparisons and build new benchmarks based on three datasets: PASCAL VOC, COCO and LVIS. Again, our fine-tuning approach establishes a new state of the art on the revised benchmarks. The code as well as the pretrained models are available at https://github.com/ucbdrive/few-shot-object-detection.
