Frustratingly Simple Few-Shot Object Detection
This addresses the problem of detecting rare objects with limited examples for computer vision applications, though it is incremental as it builds on existing fine-tuning techniques.
The paper tackles few-shot object detection by showing that fine-tuning only the last layer of existing detectors on rare classes outperforms meta-learning methods by 2-20 points and sometimes doubles prior accuracy, establishing new state-of-the-art results on revised benchmarks.
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.