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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.
The problem of learning new concepts with small number of labeled data is usually referred to as few-shot learning BID14 .
Two of the most famous datasets to address the problem of few shot classification are mini-Imagenet (Vinyals et al., 2016) and tiered-Imagenet BID19 .
Both of these datasets address the problem of few shot classification of single objects.
BID10 addresses the problem of few shot object detection in natural images.
There are two main groups of approaches addressing this problem (i) optimization-based frameworks and (ii) metric-based frameworks.
Optimization-based framework BID0 BID6 BID20 , is a class of algorithms that learn how to quickly learn new concepts.
Other notable performant approaches that do not fall into these two categories is SNAIL BID16 .Meta-learning approaches work by learning a parameterized function that maps labeled training sets into classifiers.
Metric-based framework learn a representation that minimize intra-class distances while maximize the distance between variant classes.
These approaches usually rely on an episodic training framework: the model is trained with sub-tasks (episodes) in which there are only a few training samples for each category.
Matching networks (Vinyals et al., 2016) trains a similarity function between images.
In each episode, it uses an attention mechanism (over the encoded support) as a similarity measure for one-shot classification.
In prototypical networks (Snell et al., 2017) , a metric space is learned where embeddings of queries of one category are close to the centroid (or prototype) of support of the same category, and far away from centroids of other classes in the episode.
Due to simplicity and performance of this approach, many methods extended this work.
For instance, BID19 propose a semi-supervised fewshot learning approach and show that leveraging unlabeled samples outperform purely supervised prototypical networks.
BID10 propose to augment the support set by generating hallucinated examples.
Task-dependent adaptive metric (TADAM) BID17 ) relies on conditional batch normalization BID18 to provide task adaptation (based on task representations encoded by visual features) to learn a task-dependent metric space.
In order to test the ability to disentangle unseen objects on a given image, we propose a task of multi-object few-shot classification.
Few-shot classification First, we briefly summarize the single object few-shot classification BID14 .
While a dataset for supervised learning is comprised of a number of input-label pairs, a dataset D for meta-learning is comprised of a number of tasks.
In a common K-shot N -way classification every task has a support set S = {(x i , y i )} i of the size KN and a query set DISPLAYFORM0 The learner is given a number of support-query pairs for training.
For testing, the learner is given am unseen support set S and asked to predict labels for a certain query Q = x, which label is one of the labels in the support.
Usually, prototypes are computed in the following way.
First, the images from the support set e i = CNN(x i ) are embedded, the prototype for each class is computed as an average embeding p n = mean i:yi=n e i for all n ∈ 1 . . .
N .
In this work, we propose to extend this task for a multi category case.
We define a K-shot N -way D-dense task to have the support set S = {(x i , y ) is a tuple of D labels corresponding the objects.
This way, the learner is exposed to every object class exactly K times, therefore our methods can be easier compared among varying D. Similarly, the query Q = {(x i , Y i )} i is a set of images each containing D objects with ground truth labels y i .
A naïve aproach requires exponential in D pseudo-label to represent all possible combinations.
The learner can only be exposed to a limited number of possible combinations of objects.
The exponential size of the label quickly overpasses few shots (commonly from 5 to 20) available for training.
We propose multi-prototype networks to tackle aforementioned exponential explosion.
For simplicity, in this work we assume that the labels y i are given in the order that objects appear on the image x i from left to right.
This is a major simplification and we will lift it in the near future work.
To extend the proto-net setup, we train a model to produce D embeddings for every image e .
The rest of the procedure is identical to the proto-net -every query is compared to the prototypes and the distance to the correct prototype is pushed down, all the rest are pushed up.
We aim to have a controlled environment for reliable experiments.
To achieve this, we develop a dataset based on Shapenet 3D models renderred with Blender in the setup similar to CLEVR.
This provides us flexibility to construct single or multiple object tasks and change the task parameters -number of shots and ways.
The dataset along with the generation tools will be made publically available.
In the next sections we describe in the detail the dataset generation procedure.
Then, to compare the complexity of the dataset to existing ones, we run a popular model TADAM BID17 for a traditional single-object-per-image setup.
Then, we increase the number of objects per image and report results with our proposed model.
We generated a dataset using methods similarly to visual reasoning dataset CLEVR BID9 .
To increase the variability of the object classes, we used 3D meshes from Shapenet BID3 dataset.
Beforehand, we splitted 55 Shapenet classes into three subsets randomly for training, validation and testing (see Appendix A).
The sizes of the subsets are 35, 10, 10, respectively.
We render images task-by-task.
We uniformly sample classes used in the task.
Then, we sample a mesh of each class K times.
We distribute object over canvases.
Finally, we render images by randomly placing meshes on each canvas.
In order to produce the representation of an object independent of color, we strip the texture and apply random color random texture of either rubber or metal.
FIG3 demonstrates some samples from the test split.
The model should be able to tackle We train a prototypical network and TADAM with 12-block residual feature extractor BID8 .
We summarize our results in the TAB0 and compare to performance on mini-ImageNet.
As it can be seen, our dataset is simpler than mini-ImageNet, but it cannot be trivially solved by close to state-of-the-art methods even in the simpest single-object case.
Therefore, we conclude that the dataset is not trivial nor too hard.
Having shown that the proposed dataset is sound for single-object task, we increase the number of objects per image and apply the proposed MultiProtoNet (see Section 3.1).
In all experiments below, we used 12-block residual network that produces a corresponding number of embeddings per image.
All networks are optimized with Adam (Kingma & Ba, 2014) with the Euclidian distance metric.
The experiments are summarized in the TAB1 .
We notice that while the accuracy accuracy drops significantly when transitioning from single to multiple objects, it drop as much from 2 to 3 objects.
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.

assistant: text: We introduce a diagnostic task which is a variation of few-shot learning and introduce a dataset for it.