Previous work on novel object detection considers zero or few-shot settings
where none or few examples of each category are available for training. In real
world scenarios, it is less practical to expect that 'all' the novel classes
are either unseen or {have} few-examples. Here, we propose a more realistic
setting termed 'Any-shot detection', where totally unseen and few-shot
categories can simultaneously co-occur during inference. Any-shot detection
offers unique challenges compared to conventional novel object detection such
as, a high imbalance between unseen, few-shot and seen object classes,
susceptibility to forget base-training while learning novel classes and
distinguishing novel classes from the background. To address these challenges,
we propose a unified any-shot detection model, that can concurrently learn to
detect both zero-shot and few-shot object classes. Our core idea is to use
class semantics as prototypes for object detection, a formulation that
naturally minimizes knowledge forgetting and mitigates the class-imbalance in
the label space. Besides, we propose a rebalanced loss function that emphasizes
difficult few-shot cases but avoids overfitting on the novel classes to allow
detection of totally unseen classes. Without bells and whistles, our framework
can also be used solely for Zero-shot detection and Few-shot detection tasks.
We report extensive experiments on Pascal VOC and MS-COCO datasets where our
approach is shown to provide significant improvements.

本文介绍了一种新的物体检测挑战性场景 “Any-shot detection”，并提出了一个统一的模型，可以同时检测零 / 少样本物体类别，其中核心思想是使用类语义作为目标检测的原型，并提出一种重新平衡的损失函数来强调难以检测的少样本情况，但避免在新类别上过拟合。在 Pascal VOC 和 MS-COCO 数据集上的实验表明，该方法可以显著提高检测性能。