Semantic Graph for Zero-Shot Learning

Zero-shot learning aims to classify visual objects without any training data via knowledge transfer between seen and unseen classes. This is typically achieved by exploring a semantic embedding space where the seen and unseen classes can be related. Previous works differ in what embedding space is used and how different classes and a test image can be related. In this paper, we utilize the annotation-free semantic word space for the former and focus on solving the latter issue of modeling relatedness. Specifically, in contrast to previous work which ignores the semantic relationships between seen classes and focus merely on those between seen and unseen classes, in this paper a novel approach based on a semantic graph is proposed to represent the relationships between all the seen and unseen class in a semantic word space. Based on this semantic graph, we design a special absorbing Markov chain process, in which each unseen class is viewed as an absorbing state. After incorporating one test image into the semantic graph, the absorbing probabilities from the test data to each unseen class can be effectively computed; and zero-shot classification can be achieved by finding the class label with the highest absorbing probability. The proposed model has a closed-form solution which is linear with respect to the number of test images. We demonstrate the effectiveness and computational efficiency of the proposed method over the state-of-the-arts on the AwA (animals with attributes) dataset.Comment: 9 pages, 5 figure

Organizational knowledge transfer through creation, mobilization and diffusion: A case analysis of InTouch within Schlumberger

There is a paucity of theory for the effective management of knowledge transfer within large organisations. Practitioners continue to rely upon ‘experimental’ approaches to address the problem. This research attempts to reduce the gap between theory and application, thereby improving conceptual clarity for the transfer of knowledge. The paper, through an in-depth case analysis conducted within Schlumberger, studies the adoption of an intranet-based knowledge management (KM) system (called InTouch) to support, strategically align and transfer knowledge resources. The investigation was undertaken through the adoption of a robust methodological approach (abductive strategy) incorporating the role of technology as an enabler of knowledge management application. Consequently, the study addressed the important question of translating theoretical benefits of KM into practical reality. The research formulates a set of theoretical propositions which are seen as key to the development of an effective knowledge based infrastructure. The findings identify 30 generic attributes that are essential to the creation, mobilisation and diffusion of organisational knowledge. The research makes a significant contribution to identifying a theoretical and empirically based agenda for successful intranet-based KM which will be of benefit to both the academic and practitioner communities. The paper also highlights and proposes important areas for further research

Coupled similarity analysis in supervised learning

University of Technology Sydney. Faculty of Engineering and Information Technology.In supervised learning, the distance or similarity measure is widely used in a lot of classification algorithms. When calculating the categorical data similarity, the strategy used by the traditional classifiers often overlooks the inter-relationship between different data attributes and assumes that they are independent of each other. This can be seen, for example, in the overlap similarity and the frequency based similarity. While for the numerical data, the most used Euclidean distance or Minkowski distance is restricted in each single feature and assumes the features in the dataset have no outer connections. That can cause problems in expressing the real similarity or distance between instances and may give incorrect results if the inter-relationship between attributes is ignored. The same problems exist in other supervised learning, such as the classification tasks of class-imbalance or multi-label. In order to solve these research limitations and challenges, this thesis proposes an insightful analysis on coupled similarity in supervised learning to give an expression of similarity that is more closely related to the real nature of the problem. Firstly, we propose a coupled fuzzy kNN to classify imbalanced categorical data which have strong relationships between objects, attributes and classes in Chapter 3. It incorporates the size membership of a class with attribute weight into a coupled similarity measure, which effectively extracts the intercoupling and intra-coupling relationships from categorical attributes. As it reveals the true inner-relationship between attributes, the similarity strategy we have used can make the instances of each class more compact when measured by the distance. That brings substantial benefits when dealing with class imbalance data. The experiment results show that our supposed method has a more stable and higher average performance than the classic algorithms. We also introduce a coupled similar distance for continuous features, by considering the intra-coupled relationship and inter-coupled relationship between the numerical attributes and their corresponding extensions. As detailed in Chapter 4, we calculate the coupling distance between continuous features based on discrete groups. Substantial experiments have verified that our coupled distance outperforms the original distance, and this is also supported by statistical analysis. When considering the similarity concept, people may only relate to the categorical data, while for the distance concept, people may only take into account the numerical data. Seldom have methods taken into account the both concepts, especially when considering the coupling relationship between features. In Chapter 5, we propose a new method which integrates our coupling concept for mixed type data. In our method, we first do discretization on numerical attributes to transfer such continuous values into separate groups, so as to adopt the inter-coupling distance as we do on categorical features (coupling similarity), then we combine this new coupled distance to the original distance (Euclidean distance), to overcome the shortcoming of the previous algorithms. The experiment results show some improvement when compared to the basic and some variants of kNN algorithms. We also extend our coupling concept to multi-label classification tasks. The traditional single-label classifiers are known to be not suitable for multi-label tasks anymore, owing to the overlap concept of the class labels. The most used classifier in multi-label problems, ML-kNN, learns a single classifier for each label independently, so it is actually a binary relevance classifier. As a consequence, this algorithm is often criticized. To overcome this drawback, we introduce a coupled label similarity, which explores the inner relationship between different labels in multi-label classification according to their natural co-occurrence. This similarity reflects the distance of the different classes. By integrating this similarity with the multi-label kNN algorithm, we improve the performance significantly. Evaluated over three commonly used verification criteria for multi-label classifiers, our proposed coupled multi-label classifier outperforms the ML-kNN, BR-kNN and even IBLR. The result indicates that our supposed coupled label similarity is appropriate for multi-label learning problems and can work more effectively compared to other methods. All the classifiers analyzed in this thesis are based on our coupling similarity (or distance), and applied to different tasks in supervised learning. The performance of these models is examined by widely used verification criteria, such as ROC, Accuracy Rate, Average Precision and Hamming Loss. This thesis provides insightful knowledge for investors to find the inner relationship between features in supervised learning tasks

Zero-Shot Object Recognition Based on Haptic Attributes

International audienceRobots operating in household environments need to recognize a variety of objects. Several touch-based object recognition systems have been proposed in the last few years [2]– [5]. They map haptic data to object classes using machine learning techniques, and then use the learned mapping to recognize one of the previously encountered objects. The accuracy of these proposed methods depends on the mass of the the training samples available for each object class. On the other hand, haptic data collection is often system (robot) specific and labour intensive. One way to cope with this problem is to use a knowledge transfer based system, that can exploit object relationships to share learned models between objects. However, while knowledge-based systems, such as zero shot learning [6], have been regularly proposed for visual object recognition, a similar system is not available for haptic recognition. Here we developed [1] the first haptic zero-shot learning system that enables a robot to recognize, using haptic exploration alone, objects that it encounters for the first time. Our system first uses the so called Direct Attributes Prediction (DAP) model [7] to train on the semantic representation of objects based on a list of haptic attributes, rather than the object itself. The attributes (including physical properties such as shape, texture, material) constitute an intermediate layer relating objects, and is used for knowledge transfer. Using this layering, our system can predict the attribute-based representation of a new (previously non-trained) object and use it to infer its identity. A. System Overview An overview of our system is given in Fig. 1. Given distinct training and test data-sets Y and Z, that are described by an attribute basis a, we first associate a binary label a o m to each object o with o ∈ Y ∪ Z and m = 1. .. M. This results in a binary object-attribute matrix K. For a given attributes list during training, haptic data collected from Y are used to train a binary classifier for each attribute a m. Finally, to classify a test sample x as one of Z objects, x is introduced to each one of the learned attribute classifiers and the output attributes posteriors p(a m | x) are used to predict the corresponding object, provided that the ground truth is available in K. This extended abstract is a summary of submission [1] B. Experimental Setup To collect haptic data, we use the Shadow anthropo-morphic robotic hand equipped with a BioTac multimodal tactile sensor on each fingertip. We developed a force-based grasp controller that enables the hand to enclose an object. The joint encoder readings provides us with information on object shape, while the BioTac sensors provides us with information about objects material, texture and compliance at each fingertip 1. In order to find the appropriate list of attributes describing our object set (illustrated in Fig. 2), we used online dictionaries to collect one or multiple textual definitions of each object. From this data, we extracted 11 haptic adjectives, or descriptions that could be " felt " using our robot hand. These adjectives served as our attributes: made of porcelain, made of plastic, made of glass, made of cardboard, made of stainless steel, cylindrical, round, rectangular, concave, has a handle, has a narrow part. We grouped the attributes into material attributes, and shape attributes. During the training phase, we use the Shadow hand joint readings x sh to train an SVM classifier for each shape, and BioTacs readings x b to train an SVM classifier for each material attribute. SVM training returns a distance s m (x) measure for each sample x that gives how far x lies from the discriminant hyper-plane. We transform this score to an attribute posterior p(a m | x) using a sigmoid function