Deep learning approaches usually require a large amount of labeled data to generalize. However, humans can learn a new concept only by a few samples. One of the high cogntition human capablities is to learn several concepts at the same time. In this paper, we address the task of classifying multiple objects by seeing only a few samples from each category. To the best of authors' knowledge, there is no dataset specially designed for few-shot multiclass classification. We design a task of mutli-object few class classification and an environment for easy creating controllable datasets for this task. We demonstrate that the proposed dataset is sound using a method which is an extension of prototypical networks. Deep learning approaches are usually capable of solving a classification problem when a large labeled dataset is available during the training BID12 Sutskever et al., 2014; BID4 . However, when a very few samples of a new category is shown to a trained classifier, it either fails to generalize or overfit on the new samples. Humans, however, can easily generalize their prior knowledge to learn a new concept even with one sample. Few-shot learning approaches are proposed to address this gap between human capablity of learning a new concept with only a very few labled samples and machine capablity in generalizing to a new concept. mini-ImageNet (Vinyals et al., 2016) and tiered-Imagenet BID19 are two main datasets that are developed to help the research community addressing the problem of few-shot classification. Although that human are capable of learning a new concept with only a very few samples. Learning a few new concepts at the same time, and with only a very few samples of each is considered as a high cognition task BID1 and very challenging even for humans.It is yet an active area of study to know how human are capable of doing this. There could be many factors involved in this high cognition process, and there are many hypothesis around this. One popular hypothesis is that the brain is able to learn a good representation that has high capacity and can generalize well BID7 . Studying the reasons behind human high cognitive capablity of learning a few new concepts in paralell and with only a very few samples, is out of the scope of this paper. However, in this paper, we propose to extend the few shot learning problem to multi-class few shot classification problem and moving a step towards filling the gap between human cognitive capablity of learning multiple new concepts in paralel and with only a few samples, and machine learning approaches.To do so, our first step is to define a dataset and a setup to address this problem, and an evaluation metric to measure our progression towards solving this problem.We argue that the existing datasets are not desirable for this task. Omniglot BID13 , mini-ImageNet, tiered-ImagaNet, are designed for single object classification. Such datasets as, MS COCO BID15 and Pascal VOC BID5 have multiple object classes but they are not well suited for few-shot learning. The issue is the high imbalance of class cooccurrence (for example, 'human' label occures with all other classes). Therefore it is hard to prevent the learner from "sneak peeking" new concepts.To sum it up, this work's contribution is two-fold: 1. We propose the new task of mutli-object few-shot classification to test model's ability to disentagle and represent several object on an image (see Section 3) and propose an extension to prototypical-style models to efficiently solve the task (Section 3.1);2. We construct a new dataset which provides clean and controlled environment of multiobject images (see Section 4) and provide the framework, benchmarks and the code for the community to explore controlled scenarios and alternative few-shot classification tasks. In this work we introduced a task of few-shot multi-object classification and an environment for generating datasets for this task. We compared the proposed dataset to existing ones in singleobject case. Then, we used a simple extension of prototypical networks to conduct experiments multi-object case. We believe that this task will help diagnosing metric-learning models that need to disentangle several objects on an image.One of the future directions we are taking is to lift the limitation of known object order (Section 3.1). Then we plan to use stronger feature extractors BID17 and extend the work to more natural data.
We introduce a diagnostic task which is a variation of few-shot learning and introduce a dataset for it.
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