text: Attribute based knowledge transfer has proven very successful in visual object analysis and learning previously unseen classes. However, the common approach learns and transfers attributes without taking into consideration the embedded structure between the categories in the source set. Such information provides important cues on the intraattribute variations. We propose to capture these variations in a hierarchical model that expands the knowledge source with additional abstraction levels of attributes. We also provide a novel transfer approach that can choose the appropriate attributes to be shared with an unseen class. We evaluate our approach on three public datasets: a Pascal, Animals with Attributes and CUB-200-2011 Birds. The experiments demonstrate the effectiveness of our model with significant improvement over state-of-the-art.
  @cite_3: We present a hierarchical classification model that allows rare objects to borrow statistical strength from related objects that have many training examples. Unlike many of the existing object detection and recognition systems that treat different classes as unrelated entities, our model learns both a hierarchy for sharing visual appearance across 200 object categories and hierarchical parameters. Our experimental results on the challenging object localization and detection task demonstrate that the proposed model substantially improves the accuracy of the standard single object detectors that ignore hierarchical structure altogether.
  @cite_0: In this paper we study how to perform object classification in a principled way that exploits the rich structure of real world labels. We develop a new model that allows encoding of flexible relations between labels. We introduce Hierarchy and Exclusion (HEX) graphs, a new formalism that captures semantic relations between any two labels applied to the same object: mutual exclusion, overlap and subsumption. We then provide rigorous theoretical analysis that illustrates properties of HEX graphs such as consistency, equivalence, and computational implications of the graph structure. Next, we propose a probabilistic classification model based on HEX graphs and show that it enjoys a number of desirable properties. Finally, we evaluate our method using a large-scale benchmark. Empirical results demonstrate that our model can significantly improve object classification by exploiting the label relations.
  @cite_23: ImageNet is a large-scale database of object classes with millions of images. Unfortunately only a small fraction of them is manually annotated with bounding-boxes. This prevents useful developments, such as learning reliable object detectors for thousands of classes. In this paper we propose to automatically populate ImageNet with many more bounding-boxes, by leveraging existing manual annotations. The key idea is to localize objects of a target class for which annotations are not available, by transferring knowledge from related source classes with available annotations. We distinguish two kinds of source classes: ancestors and siblings. Each source provides knowledge about the plausible location, appearance and context of the target objects, which induces a probability distribution over windows in images of the target class. We learn to combine these distributions so as to maximize the location accuracy of the most probable window. Finally, we employ the combined distribution in a procedure to jointly localize objects in all images of the target class. Through experiments on 0.5 million images from 219 classes we show that our technique (i) annotates a wide range of classes with bounding-boxes; (ii) effectively exploits the hierarchical structure of ImageNet, since all sources and types of knowledge we propose contribute to the results; (iii) scales efficiently.
  @cite_16: We introduce a new method for building classification models when we have prior knowledge of how the classes can be arranged in a hierarchy, based on how easily they can be distinguished. The new method uses a Bayesian form of the multinomial logit (MNL, a.k.a. “softmax”) model, with a prior that introduces correlations between the parameters for classes that are nearby in the tree. We compare the performance on simulated data of the new method, the ordinary MNL model, and a model that uses the hierarchy in different way. We also test the new method on a document labelling problem, and find that it performs better than the other methods, particularly when the amount of training data is small.
  @cite_25: Learning from few examples is considered a very challenging task where transfer learning proved to be beneficial. Such a learning framework exploits previous experiences and knowledge to compensate for the lack of training data in a novel domain. Knowledge representation plays a vital role in the type and performance of transfer learning approaches, as well as its robustness against negative transfer effect. This aspect is usually not considered in most of the proposed transfer learning methodologies, where the focus is either on the transfer type or on the representation. In this work, we study the use of various high-level semantics in transfer metric learning. We propose a generic transfer metric learning framework, and analyze the effect of different semantic similarity spaces on transfer type and efficiency against negative transfer. Furthermore, we introduce a hierarchical knowledge representation model based on the embedded structure in the attribute semantic space. The evaluation of the framework on challenging transfer settings in the context of action similarity demonstrates the effectiveness of our approach.
  @cite_11: Attributes are an intermediate representation, which enables parameter sharing between classes, a must when training data is scarce. We propose to view attribute-based image classification as a label-embedding problem: each class is embedded in the space of attribute vectors. We introduce a function which measures the compatibility between an image and a label embedding. The parameters of this function are learned on a training set of labeled samples to ensure that, given an image, the correct classes rank higher than the incorrect ones. Results on the Animals With Attributes and Caltech-UCSD-Birds datasets show that the proposed framework outperforms the standard Direct Attribute Prediction baseline in a zero-shot learning scenario. The label embedding framework offers other advantages such as the ability to leverage alternative sources of information in addition to attributes (e.g. class hierarchies) or to transition smoothly from zero-shot learning to learning with large quantities of data.
summary: Additionally, hierarchies represent an attractive structure for knowledge transfer and they have been exploited in various ways: parameter transfer @cite_16 @cite_3 , representation transfer @cite_25 and bounding box annotations propagation @cite_23 . Of particular relevance to our work is the joint modeling of hierarchy and attributes. In @cite_11 , a ranking classifier is trained using attributes for label embedding; showing that using hierarchical labels along attributes as side information improved the zero-shot performance. In the recent work of @cite_0 , a hierarchy and exclusion graph is learned over the various object categories. The graph models binary relations among the classes like mutual exclusion and overlap. They also model similar relations between objects and attributes and use it for zero-shot classification.