Energy Confused Adversarial Metric Learning for Zero-Shot Image Retrieval and Clustering

Deep metric learning has been widely applied in many computer vision tasks, and recently, it is more attractive in \emph{zero-shot image retrieval and clustering}(ZSRC) where a good embedding is requested such that the unseen classes can be distinguished well. Most existing works deem this 'good' embedding just to be the discriminative one and thus race to devise powerful metric objectives or hard-sample mining strategies for leaning discriminative embedding. However, in this paper, we first emphasize that the generalization ability is a core ingredient of this 'good' embedding as well and largely affects the metric performance in zero-shot settings as a matter of fact. Then, we propose the Energy Confused Adversarial Metric Learning(ECAML) framework to explicitly optimize a robust metric. It is mainly achieved by introducing an interesting Energy Confusion regularization term, which daringly breaks away from the traditional metric learning idea of discriminative objective devising, and seeks to 'confuse' the learned model so as to encourage its generalization ability by reducing overfitting on the seen classes. We train this confusion term together with the conventional metric objective in an adversarial manner. Although it seems weird to 'confuse' the network, we show that our ECAML indeed serves as an efficient regularization technique for metric learning and is applicable to various conventional metric methods. This paper empirically and experimentally demonstrates the importance of learning embedding with good generalization, achieving state-of-the-art performances on the popular CUB, CARS, Stanford Online Products and In-Shop datasets for ZSRC tasks. \textcolor[rgb]{1, 0, 0}{Code available at http://www.bhchen.cn/}.Comment: AAAI 2019, Spotligh

Ensemble of Loss Functions to Improve Generalizability of Deep Metric Learning methods

Deep Metric Learning (DML) learns a non-linear semantic embedding from input data that brings similar pairs together while keeps dissimilar data away from each other. To this end, many different methods are proposed in the last decade with promising results in various applications. The success of a DML algorithm greatly depends on its loss function. However, no loss function is perfect, and it deals only with some aspects of an optimal similarity embedding. Besides, the generalizability of the DML on unseen categories during the test stage is an important matter that is not considered by existing loss functions. To address these challenges, we propose novel approaches to combine different losses built on top of a shared deep feature extractor. The proposed ensemble of losses enforces the deep model to extract features that are consistent with all losses. Since the selected losses are diverse and each emphasizes different aspects of an optimal semantic embedding, our effective combining methods yield a considerable improvement over any individual loss and generalize well on unseen categories. Here, there is no limitation in choosing loss functions, and our methods can work with any set of existing ones. Besides, they can optimize each loss function as well as its weight in an end-to-end paradigm with no need to adjust any hyper-parameter. We evaluate our methods on some popular datasets from the machine vision domain in conventional Zero-Shot-Learning (ZSL) settings. The results are very encouraging and show that our methods outperform all baseline losses by a large margin in all datasets.Comment: 27 pages, 12 figure

Visual Representation Learning with Limited Supervision

The quality of a Computer Vision system is proportional to the rigor of data representation it is built upon. Learning expressive representations of images is therefore the centerpiece to almost every computer vision application, including image search, object detection and classification, human re-identification, object tracking, pose understanding, image-to-image translation, and embodied agent navigation to name a few. Deep Neural Networks are most often seen among the modern methods of representation learning. The limitation is, however, that deep representation learning methods require extremely large amounts of manually labeled data for training. Clearly, annotating vast amounts of images for various environments is infeasible due to cost and time constraints. This requirement of obtaining labeled data is a prime restriction regarding pace of the development of visual recognition systems. In order to cope with the exponentially growing amounts of visual data generated daily, machine learning algorithms have to at least strive to scale at a similar rate. The second challenge consists in the learned representations having to generalize to novel objects, classes, environments and tasks in order to accommodate to the diversity of the visual world. Despite the evergrowing number of recent publications tangentially addressing the topic of learning generalizable representations, efficient generalization is yet to be achieved. This dissertation attempts to tackle the problem of learning visual representations that can generalize to novel settings while requiring few labeled examples. In this research, we study the limitations of the existing supervised representation learning approaches and propose a framework that improves the generalization of learned features by exploiting visual similarities between images which are not captured by provided manual annotations. Furthermore, to mitigate the common requirement of large scale manually annotated datasets, we propose several approaches that can learn expressive representations without human-attributed labels, in a self-supervised fashion, by grouping highly-similar samples into surrogate classes based on progressively learned representations. The development of computer vision as science is preconditioned upon the seamless ability of a machine to record and disentangle pictures' attributes that were expected to only be conceived by humans. As such, particular interest was dedicated to the ability to analyze the means of artistic expression and style which depicts a more complex task than merely breaking an image down to colors and pixels. The ultimate test for this ability is the task of style transfer which involves altering the style of an image while keeping its content. An effective solution of style transfer requires learning such image representation which would allow disentangling image style and its content. Moreover, particular artistic styles come with idiosyncrasies that affect which content details should be preserved and which discarded. Another pitfall here is that it is impossible to get pixel-wise annotations of style and how the style should be altered. We address this problem by proposing an unsupervised approach that enables encoding the image content in such a way that is required by a particular style. The proposed approach exchanges the style of an input image by first extracting the content representation in a style-aware way and then rendering it in a new style using a style-specific decoder network, achieving compelling results in image and video stylization. Finally, we combine supervised and self-supervised representation learning techniques for the task of human and animals pose understanding. The proposed method enables transfer of the representation learned for recognition of human poses to proximal mammal species without using labeled animal images. This approach is not limited to dense pose estimation and could potentially enable autonomous agents from robots to self-driving cars to retrain themselves and adapt to novel environments based on learning from previous experiences