Evaluation of Output Embeddings for Fine-Grained Image Classification

Image classification has advanced significantly in recent years with the availability of large-scale image sets. However, fine-grained classification remains a major challenge due to the annotation cost of large numbers of fine-grained categories. This project shows that compelling classification performance can be achieved on such categories even without labeled training data. Given image and class embeddings, we learn a compatibility function such that matching embeddings are assigned a higher score than mismatching ones; zero-shot classification of an image proceeds by finding the label yielding the highest joint compatibility score. We use state-of-the-art image features and focus on different supervised attributes and unsupervised output embeddings either derived from hierarchies or learned from unlabeled text corpora. We establish a substantially improved state-of-the-art on the Animals with Attributes and Caltech-UCSD Birds datasets. Most encouragingly, we demonstrate that purely unsupervised output embeddings (learned from Wikipedia and improved with fine-grained text) achieve compelling results, even outperforming the previous supervised state-of-the-art. By combining different output embeddings, we further improve results.Comment: @inproceedings {ARWLS15, title = {Evaluation of Output Embeddings for Fine-Grained Image Classification}, booktitle = {IEEE Computer Vision and Pattern Recognition}, year = {2015}, author = {Zeynep Akata and Scott Reed and Daniel Walter and Honglak Lee and Bernt Schiele}

Concept graphs: Applications to biomedical text categorization and concept extraction

As science advances, the underlying literature grows rapidly providing valuable knowledge mines for researchers and practitioners. The text content that makes up these knowledge collections is often unstructured and, thus, extracting relevant or novel information could be nontrivial and costly. In addition, human knowledge and expertise are being transformed into structured digital information in the form of vocabulary databases and ontologies. These knowledge bases hold substantial hierarchical and semantic relationships of common domain concepts. Consequently, automating learning tasks could be reinforced with those knowledge bases through constructing human-like representations of knowledge. This allows developing algorithms that simulate the human reasoning tasks of content perception, concept identification, and classification. This study explores the representation of text documents using concept graphs that are constructed with the help of a domain ontology. In particular, the target data sets are collections of biomedical text documents, and the domain ontology is a collection of predefined biomedical concepts and relationships among them. The proposed representation preserves those relationships and allows using the structural features of graphs in text mining and learning algorithms. Those features emphasize the significance of the underlying relationship information that exists in the text content behind the interrelated topics and concepts of a text document. The experiments presented in this study include text categorization and concept extraction applied on biomedical data sets. The experimental results demonstrate how the relationships extracted from text and captured in graph structures can be used to improve the performance of the aforementioned applications. The discussed techniques can be used in creating and maintaining digital libraries through enhancing indexing, retrieval, and management of documents as well as in a broad range of domain-specific applications such as drug discovery, hypothesis generation, and the analysis of molecular structures in chemoinformatics

On the Effect of Semantically Enriched Context Models on Software Modularization

Many of the existing approaches for program comprehension rely on the linguistic information found in source code, such as identifier names and comments. Semantic clustering is one such technique for modularization of the system that relies on the informal semantics of the program, encoded in the vocabulary used in the source code. Treating the source code as a collection of tokens loses the semantic information embedded within the identifiers. We try to overcome this problem by introducing context models for source code identifiers to obtain a semantic kernel, which can be used for both deriving the topics that run through the system as well as their clustering. In the first model, we abstract an identifier to its type representation and build on this notion of context to construct contextual vector representation of the source code. The second notion of context is defined based on the flow of data between identifiers to represent a module as a dependency graph where the nodes correspond to identifiers and the edges represent the data dependencies between pairs of identifiers. We have applied our approach to 10 medium-sized open source Java projects, and show that by introducing contexts for identifiers, the quality of the modularization of the software systems is improved. Both of the context models give results that are superior to the plain vector representation of documents. In some cases, the authoritativeness of decompositions is improved by 67%. Furthermore, a more detailed evaluation of our approach on JEdit, an open source editor, demonstrates that inferred topics through performing topic analysis on the contextual representations are more meaningful compared to the plain representation of the documents. The proposed approach in introducing a context model for source code identifiers paves the way for building tools that support developers in program comprehension tasks such as application and domain concept location, software modularization and topic analysis