Few-shot Learning: Towards localization and classification of objects
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Abstract
When we have dataset with large number of labelled examples it is easy to perform object
detection task but, rare object detection from a few examples is a new problem. Metalearning
has been shown to be a promising strategy in the past. However, fine-tuning
strategies have received little attention. We discovered that fine-tuning the last layer of
detector is a critical task in few-shot object detection. On current benchmarks, such a basic
strategy outperforms meta-learning approaches by about 4 to 16 points and sometimes the
accuracy is doubled when compared to existing methodologies. However, current
benchmarks are frequently unreliable because of the significant variance in the few samples.
To generate consistent comparisons, we change the evaluation processes by choosing various
sets of training examples. The model has been evaluated on three datasets: COCO, LVIS, and
PASCAL VOC. Our fine-tuning approach amalgamated with the Ranking based loss function
which can be used for both classification and localization is state-of-the-art.
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