Robust Place Categorization With Deep Domain Generalization

Traditional place categorization approaches in robot vision assume that training and test images have similar visual appearance. Therefore, any seasonal, illumination, and environmental changes typically lead to severe degradation in performance. To cope with this problem, recent works have been proposed to adopt domain adaptation techniques. While effective, these methods assume that some prior information about the scenario where the robot will operate is available at training time. Unfortunately, in many cases, this assumption does not hold, as we often do not know where a robot will be deployed. To overcome this issue, in this paper, we present an approach that aims at learning classification models able to generalize to unseen scenarios. Specifically, we propose a novel deep learning framework for domain generalization. Our method develops from the intuition that, given a set of different classification models associated to known domains (e.g., corresponding to multiple environments, robots), the best model for a new sample in the novel domain can be computed directly at test time by optimally combining the known models. To implement our idea, we exploit recent advances in deep domain adaptation and design a convolutional neural network architecture with novel layers performing a weighted version of batch normalization. Our experiments, conducted on three common datasets for robot place categorization, confirm the validity of our contribution

Automatic Discovery, Association Estimation and Learning of Semantic Attributes for a Thousand Categories

Attribute-based recognition models, due to their impressive performance and their ability to generalize well on novel categories, have been widely adopted for many computer vision applications. However, usually both the attribute vocabulary and the class-attribute associations have to be provided manually by domain experts or large number of annotators. This is very costly and not necessarily optimal regarding recognition performance, and most importantly, it limits the applicability of attribute-based models to large scale data sets. To tackle this problem, we propose an end-to-end unsupervised attribute learning approach. We utilize online text corpora to automatically discover a salient and discriminative vocabulary that correlates well with the human concept of semantic attributes. Moreover, we propose a deep convolutional model to optimize class-attribute associations with a linguistic prior that accounts for noise and missing data in text. In a thorough evaluation on ImageNet, we demonstrate that our model is able to efficiently discover and learn semantic attributes at a large scale. Furthermore, we demonstrate that our model outperforms the state-of-the-art in zero-shot learning on three data sets: ImageNet, Animals with Attributes and aPascal/aYahoo. Finally, we enable attribute-based learning on ImageNet and will share the attributes and associations for future research.Comment: Accepted as a conference paper at CVPR 201

Searching for hidden-web databases

Journal ArticleRecently, there has been increased interest in the retrieval and integration of hidden-Web data with a view to leverage high-quality information available in online databases. Although previous works have addressed many aspects of the actual integration, including matching form schemata and automatically filling out forms, the problem of locating relevant data sources has been largely overlooked. Given the dynamic nature of the Web, where data sources are constantly changing, it is crucial to automatically discover these resources. However, considering the number of documents on the Web (Google already indexes over 8 billion documents), automatically finding tens, hundreds or even thousands of forms that are relevant to the integration task is really like looking for a few needles in a haystack. Besides, since the vocabulary and structure of forms for a given domain are unknown until the forms are actually found, it is hard to define exactly what to look for. We propose a new crawling strategy to automatically locate hidden-Web databases which aims to achieve a balance between the two conflicting requirements of this problem: the need to perform a broad search while at the same time avoiding the need to crawl a large number of irrelevant pages. The proposed strategy does that by focusing the crawl on a given topic; by judiciously choosing links to follow within a topic that are more likely to lead to pages that contain forms; and by employing appropriate stopping criteria. We describe the algorithms underlying this strategy and an experimental evaluation which shows that our approach is both effective and efficient, leading to larger numbers of forms retrieved as a function of the number of pages visited than other crawlers