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Large-Scale Long-Tailed Recognition in an Open World

Existing Computer Vision Setting v.s. Real-World Scenario

One day, an ecologist came to us. He wanted to use modern computer vision
techniques to perform automatic animal identification in his wildlife camera
trap image datasets. We were so confident because it sounded just like a basic
image classification problem. However, we failed. The dataset he provided was
extremely long-tailed and open-ended. As usual, when we did not have enough
training data, we asked if it was possible to provide more data for the tail
classes and just ignore the open classes that might appear in the testing
dataset. Unfortunately, collecting more data was not the option. It could take
an extremely long time for these ecologists to take photos of rare and secluded
animals in the wild. For some endangered animals, they even had to wait for
years for one single shot. At the same time, new animal species kept coming in,
and old animal species kept leaving. The total class number was never fixed in
such a dynamic system. Moreover, the identification of rare and new animals has
more conservational values than abundant animals. If we could only do well on
the abundant classes, the method would never be practically usable. We tried
all possible methods we could think of (data augmentation, sampling techniques,
few-shot learning, imbalanced classification, etc.); but none of the existing
methods could handle abundant classes, scarce classes and open classes at the
same time (Fig. 1).



Figure 1: There exists a considerable gap between the existing computer vision
setting and the real-world scenario.

Since then, we have been thinking, what is the biggest reason for this gap
between existing computer vision methods and real-world scenarios?
Similar
situations don’t just happen in wildlife image data, they happen over and over
again in real-world scenarios (both in the industry and in the academia). If
convolutional neural networks can classify images from the massive ImageNet
dataset so well, why image classification is still an unsolved problem in an
open world? Almost every task (e.g. few-shot learning and open set recognition)
proposed in visual recognition field has successful approaches, but it seems
that no one has tried to see those problems as a whole. When it comes to
real-world applications, classification tasks (either for head classes or for
tail classes) sometimes do not just come alone. Therefore, we think that the
gap may come from the problem setting of visual recognition itself.

Open Long-Tailed Recognition (OLTR)

In existing visual recognition setting, the training data and testing data are
both balanced under a closed-world setting, e.g. the ImageNet dataset. However,
this setting is not a good proxy of the real-world scenario. For example, it is
never possible for ecologists to gather balanced wildlife datasets because
animal distribution is imbalanced. Similarly, people are bothered by the
imbalanced and open-ended distribution from all sorts of datasets: street
signs, fashion brands, faces, weather conditions, street conditions, etc. To
faithfully reflect these aspects, we formally study “Open Long-Tailed
Recognition” (OLTR)
arising in natural data settings. A practical system
shall be able to classify among a few common and many rare categories, to
generalize the concept of a single category from only a few known instances,
and to acknowledge novelty upon an instance of a never seen category. We
define OLTR as learning from long-tail and open-end distributed data and
evaluating the classification accuracy over a balanced test set which includes
head, tail, and open classes in a continuous spectrum (Fig. 2).



Figure 2: Our task of open long-tailed recognition must learn from long-tail
distributed training data in an open world and deal with imbalanced
classification, few-shot learning, and open-set recognition over the entire
spectrum.

While OLTR has not been defined in the literature, there are three closely
related tasks which are often studied in isolation: imbalanced classification,
few-shot learning, and open-set recognition. Fig. 3 summarizes their
differences. The newly proposed Open Long-Tailed Recognition (OLTR) serves as a
more comprehensive and more realistic touchstone for evaluating visual
recognition systems.



Figure 3: The differences between imbalanced classification, few-shot learning,
open set recognition and open long-tailed recognition (OLTR).

The Importance of Attention & Memory

We propose to map an image to a feature space such that visual concepts can
easily relate to each other based on a learned metric that respects the
closed-world classification while acknowledging the novelty of the open world.
Our proposed dynamic meta-embedding combines a direct image feature and an
associated memory feature, with the feature norm indicating the familiarity to
known classes, as illustrated in Fig. 4.

Firstly, we obtain a visual memory by aggregating the knowledge from both head
and tail classes. Then the visual concepts stored in the memory are infused
back as associated memory feature to enhance the original direct feature. It
can be understood as using induced knowledge (i.e. memory feature) to assist
the direct observation (i.e. direct feature). We further learn a concept
selector to control the amount and type of memory feature to be infused. Since
head classes already have an abundant direct observation, only a small amount
of memory feature is infused for them. On the contrary, tail classes suffer
from scarce observation, the associated visual concepts in memory feature are
extremely beneficial. Finally, we calibrate the confidence of open classes by
calculating their reachability to the obtained visual memory.



Figure 4: Intuition explanation of our approach. Our proposed dynamic
meta-embedding combines a direct image feature and an associated memory
feature, with the feature norm indicating the familiarity to known classes.

Across-the-board Improvements

As demonstrated in Fig. 5, our approach provides comprehensive treatment to all
the many/medium/few-shot classes as well as the open classes, achieving
substantial improvements on all aspects.



Figure 5: The absolute F1 score of our method over the plain model. Ours has
across-the-board performance gains w.r.t. many/medium/few-shot and open
classes.

Visualization of Learning Dynamics

Here we inspect the visual concepts that memory feature has infused by
visualizing its top activating neurons as shown in Fig. 6. Specifically, for
each input image, we identify its top-3 transferred neurons in memory feature.
And each neuron is visualized by a collection of highest activated patches over
the whole training set. For example, to classify the top left image which
belongs to a tail class “cock”, our approach has learned to transfer visual
concepts that represent “bird head”, “round shape” and “dotted texture”
respectively. After feature infusion, the dynamic meta-embedding becomes more
informative and discriminative.



Figure 6: Examples of the top-3 infused visual concepts from memory feature.
Except for the bottom right failure case (marked in red), all the other three
input images are misclassified by the plain model and correctly classified by
our model. For example, to classify the top left image which belongs to a tail
class ‘cock’, our approach has learned to transfer visual concepts that
represent “bird head”, “round shape” and “dotted texture” respectively.

Back to the Real World

Now we go back to the real jungle and apply our proposed approach to the
wildlife data came with the ecologist mentioned in the first section.
Fortunately, our new framework achieves a substantial improvement on scarce
classes without a sacrifice of the abundant classes. More specifically, we
obtain around 40% performance gains (from 25% to 66%) on classes with less than
40 images. And we also obtain over 15% performance gains for open class
detection.

We believe computational methods developed under open long-tailed recognition
setting can ultimately satisfy the needs of natural-distributed datasets. In
summary, Open Long-Tailed Recognition (OLTR) serves as a more comprehensive and
more realistic touchstone for evaluating visual recognition systems, which can
be further extended into detection, segmentation and reinforcement learning.

Acknowledgements: We thank all co-authors of the paper “Large-Scale Long-Tailed
Recognition in an Open World” for their contributions and discussions in
preparing this blog. The views and opinions expressed in this blog are solely
of the authors of this paper.

This blog post is based on the following paper which will be presented at IEEE
Conference on Computer Vision and Pattern Recognition (CVPR 2019) as an oral
presentation:

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.