Learning from limited labeled data - Zero-Shot and Few-Shot Learning

Human beings have the remarkable ability to recognize novel visual concepts after observing only few or zero examples of them. Deep learning, however, often requires a large amount of labeled data to achieve a good performance. Labeled instances are expensive, difficult and even infeasible to obtain because the distribution of training instances among labels naturally exhibits a long tail. Therefore, it is of great interest to investigate how to learn efficiently from limited labeled data. This thesis concerns an important subfield of learning from limited labeled data, namely, low-shot learning. The setting assumes the availability of many labeled examples from known classes and the goal is to learn novel classes from only a few~(few-shot learning) or zero~(zero-shot learning) training examples of them. To this end, we have developed a series of multi-modal learning approaches to facilitate the knowledge transfer from known classes to novel classes for a wide range of visual recognition tasks including image classification, semantic image segmentation and video action recognition. More specifically, this thesis mainly makes the following contributions. First, as there is no agreed upon zero-shot image classification benchmark, we define a new benchmark by unifying both the evaluation protocols and data splits of publicly available datasets. Second, in order to tackle the labeled data scarcity, we propose feature generation frameworks that synthesize data in the visual feature space for novel classes. Third, we extend zero-shot learning and few-shot learning to the semantic segmentation task and propose a challenging benchmark for it. We show that incorporating semantic information into a semantic segmentation network is effective in segmenting novel classes. Finally, we develop better video representation for the few-shot video classification task and leverage weakly-labeled videos by an efficient retrieval method.Menschen haben die bemerkenswerte Fähigkeit, neuartige visuelle Konzepte zu erkennen, nachdem sie nur wenige oder gar keine Beispiele davon beobachtet haben. Tiefes Lernen erfordert jedoch oft eine große Menge an beschrifteten Daten, um eine gute Leistung zu erzielen. Etikettierte Instanzen sind teuer, schwierig und sogar undurchführbar, weil die Verteilung der Trainingsinstanzen auf die Etiketten naturgemäß einen langen Schwanz aufweist. Daher ist es von großem Interesse zu untersuchen, wie man effizient aus begrenzten gelabelten Daten lernen kann. Diese These betrifft einen wichtigen Teilbereich des Lernens aus begrenzt gelabelten Daten, nämlich das Low-Shot-Lernen. Das Setting setzt die Verfügbarkeit vieler gelabelter Beispiele aus bekannten Klassen voraus, und das Ziel ist es, neuartige Klassen aus nur wenigen (few-shot learning) oder null (zero-shot learning) Trainingsbeispielen davon zu lernen. Zu diesem Zweck haben wir eine Reihe von multimodalen Lernansätzen entwickelt, um den Wissenstransfer von bekannten Klassen zu neuartigen Klassen für ein breites Spektrum von visuellen Erkennungsaufgaben zu erleichtern, darunter Bildklassifizierung, semantische Bildsegmentierung und Videoaktionserkennung. Genauer gesagt, leistet diese Arbeit hauptsächlich die folgenden Beiträge. Da es keinen vereinbarten Benchmark für die Zero-Shot- Bildklassifikation gibt, definieren wir zunächst einen neuen Benchmark, indem wir sowohl die Evaluierungsprotokolle als auch die Datensplits öffentlich zugänglicher Datensätze vereinheitlichen. Zweitens schlagen wir zur Bewältigung der etikettierten Datenknappheit einen Rahmen für die Generierung von Merkmalen vor, der Daten im visuellen Merkmalsraum für neuartige Klassen synthetisiert. Drittens dehnen wir das Zero-Shot-Lernen und das few-Shot-Lernen auf die semantische Segmentierungsaufgabe aus und schlagen dafür einen anspruchsvollen Benchmark vor. Wir zeigen, dass die Einbindung semantischer Informationen in ein semantisches Segmentierungsnetz bei der Segmentierung neuartiger Klassen effektiv ist. Schließlich entwickeln wir eine bessere Videodarstellung für die Klassifizierungsaufgabe ”few-shot video” und nutzen schwach markierte Videos durch eine effiziente Abrufmethode.Max Planck Institute Informatic

f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning

When labeled training data is scarce, a promising data augmentation approach is to generate visual features of unknown classes using their attributes. To learn the class conditional distribution of CNN features, these models rely on pairs of image features and class attributes. Hence, they can not make use of the abundance of unlabeled data samples. In this paper, we tackle any-shot learning problems i.e. zero-shot and few-shot, in a unified feature generating framework that operates in both inductive and transductive learning settings. We develop a conditional generative model that combines the strength of VAE and GANs and in addition, via an unconditional discriminator, learns the marginal feature distribution of unlabeled images. We empirically show that our model learns highly discriminative CNN features for five datasets, i.e. CUB, SUN, AWA and ImageNet, and establish a new state-of-the-art in any-shot learning, i.e. inductive and transductive (generalized) zero- and few-shot learning settings. We also demonstrate that our learned features are interpretable: we visualize them by inverting them back to the pixel space and we explain them by generating textual arguments of why they are associated with a certain label.Comment: Accepted at CVPR 201

Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly

Due to the importance of zero-shot learning, i.e. classifying images where there is a lack of labeled training data, the number of proposed approaches has recently increased steadily. We argue that it is time to take a step back and to analyze the status quo of the area. The purpose of this paper is three-fold. First, given the fact that there is no agreed upon zero-shot learning benchmark, we first define a new benchmark by unifying both the evaluation protocols and data splits of publicly available datasets used for this task. This is an important contribution as published results are often not comparable and sometimes even flawed due to, e.g. pre-training on zero-shot test classes. Moreover, we propose a new zero-shot learning dataset, the Animals with Attributes 2 (AWA2) dataset which we make publicly available both in terms of image features and the images themselves. Second, we compare and analyze a significant number of the state-of-the-art methods in depth, both in the classic zero-shot setting but also in the more realistic generalized zero-shot setting. Finally, we discuss in detail the limitations of the current status of the area which can be taken as a basis for advancing it.Comment: Accepted by TPAMI in July, 2018. We introduce Proposed Split Version 2.0 (Please download it from our project webpage). arXiv admin note: substantial text overlap with arXiv:1703.0439

Learning Prototype Classifiers for Long-Tailed Recognition

The problem of long-tailed recognition (LTR) has received attention in recent years due to the fundamental power-law distribution of objects in the real-world. Most recent works in LTR use softmax classifiers that have a tendency to correlate classifier norm with the amount of training data for a given class. On the other hand, Prototype classifiers do not suffer from this shortcoming and can deliver promising results simply using Nearest-Class-Mean (NCM), a special case where prototypes are empirical centroids. However, the potential of Prototype classifiers as an alternative to softmax in LTR is relatively underexplored. In this work, we propose Prototype classifiers, which jointly learn prototypes that minimize average cross-entropy loss based on probability scores from distances to prototypes. We theoretically analyze the properties of Euclidean distance based prototype classifiers that leads to stable gradient-based optimization which is robust to outliers. We further enhance Prototype classifiers by learning channel-dependent temperature parameters to enable independent distance scales along each channel. Our analysis shows that prototypes learned by Prototype classifiers are better separated than empirical centroids. Results on four long-tailed recognition benchmarks show that Prototype classifier outperforms or is comparable to the state-of-the-art methods.Comment: Accepted at IJCAI-2

Learning Graph Embeddings for Compositional Zero-shot Learning

In compositional zero-shot learning, the goal is to recognize unseen compositions (e.g. old dog) of observed visual primitives states (e.g. old, cute) and objects (e.g. car, dog) in the training set. This is challenging because the same state can for example alter the visual appearance of a dog drastically differently from a car. As a solution, we propose a novel graph formulation called Compositional Graph Embedding (CGE) that learns image features, compositional classifiers, and latent representations of visual primitives in an end-to-end manner. The key to our approach is exploiting the dependency between states, objects, and their compositions within a graph structure to enforce the relevant knowledge transfer from seen to unseen compositions. By learning a joint compatibility that encodes semantics between concepts, our model allows for generalization to unseen compositions without relying on an external knowledge base like WordNet. We show that in the challenging generalized compositional zero-shot setting our CGE significantly outperforms the state of the art on MIT-States and UT-Zappos. We also propose a new benchmark for this task based on the recent GQA dataset.Comment: Accepted in IEEE CVPR 202

Prototype-based Incremental Few-Shot Segmentation

Semantic segmentation models have two fundamental weaknesses: i) they require large training sets with costly pixel-level annotations, and ii) they have a static output space, constrained to the classes of the training set. Toward addressing both problems, we introduce a new task, Incremental Few-Shot Segmentation (iFSS). The goal of iFSS is to extend a pretrained segmentation model with new classes from few annotated images and without access to old training data. To overcome the limitations of existing models iniFSS, we propose Prototype-based Incremental Few-Shot Segmentation (PIFS) that couples prototype learning and knowledge distillation. PIFS exploits prototypes to initialize the classifiers of new classes, fine-tuning the network to refine its features representation. We design a prototype-based distillation loss on the scores of both old and new class prototypes to avoid overfitting and forgetting, and batch-renormalization to cope with non-i.i.d.few-shot data. We create an extensive benchmark for iFSS showing that PIFS outperforms several few-shot and incremental learning methods in all scenarios

Attribute Prototype Network for Zero-Shot Learning

From the beginning of zero-shot learning research, visual attributes have been shown to play an important role. In order to better transfer attribute-based knowledge from known to unknown classes, we argue that an image representation with integrated attribute localization ability would be beneficial for zero-shot learning. To this end, we propose a novel zero-shot representation learning framework that jointly learns discriminative global and local features using only class-level attributes. While a visual-semantic embedding layer learns global features, local features are learned through an attribute prototype network that simultaneously regresses and decorrelates attributes from intermediate features. We show that our locality augmented image representations achieve a new state-of-the-art on three zero-shot learning benchmarks. As an additional benefit, our model points to the visual evidence of the attributes in an image, e.g. for the CUB dataset, confirming the improved attribute localization ability of our image representation.Comment: NeurIPS 2020. The code is publicly available at https://wenjiaxu.github.io/APN-ZSL