24,045 research outputs found
Zero-Annotation Object Detection with Web Knowledge Transfer
Object detection is one of the major problems in computer vision, and has
been extensively studied. Most of the existing detection works rely on
labor-intensive supervision, such as ground truth bounding boxes of objects or
at least image-level annotations. On the contrary, we propose an object
detection method that does not require any form of human annotation on target
tasks, by exploiting freely available web images. In order to facilitate
effective knowledge transfer from web images, we introduce a multi-instance
multi-label domain adaption learning framework with two key innovations. First
of all, we propose an instance-level adversarial domain adaptation network with
attention on foreground objects to transfer the object appearances from web
domain to target domain. Second, to preserve the class-specific semantic
structure of transferred object features, we propose a simultaneous transfer
mechanism to transfer the supervision across domains through pseudo strong
label generation. With our end-to-end framework that simultaneously learns a
weakly supervised detector and transfers knowledge across domains, we achieved
significant improvements over baseline methods on the benchmark datasets.Comment: Accepted in ECCV 201
Introduction to the special issue on cross-language algorithms and applications
With the increasingly global nature of our everyday interactions, the need for multilingual technologies to support efficient and efective information access and communication cannot be overemphasized. Computational modeling of language has been the focus of
Natural Language Processing, a subdiscipline of Artificial Intelligence. One of the current challenges for this discipline is to design methodologies and algorithms that are cross-language in order to create multilingual technologies rapidly. The goal of this JAIR special
issue on Cross-Language Algorithms and Applications (CLAA) is to present leading research in this area, with emphasis on developing unifying themes that could lead to the development of the science of multi- and cross-lingualism. In this introduction, we provide the reader with the motivation for this special issue and summarize the contributions of the papers that have been included. The selected papers cover a broad range of cross-lingual technologies including machine translation, domain and language adaptation for sentiment
analysis, cross-language lexical resources, dependency parsing, information retrieval and knowledge representation. We anticipate that this special issue will serve as an invaluable resource for researchers interested in topics of cross-lingual natural language processing.Postprint (published version
Sharing Cultural Heritage: the Clavius on the Web Project
In the last few years the amount of manuscripts digitized and made available on the Web has been constantly increasing. However, there is still a considarable lack of results concerning both the explicitation of their content and the tools developed to make it available. The objective of the Clavius on the Web project is to develop a Web platform exposing a selection of Christophorus Clavius letters along with three different levels of analysis: linguistic, lexical and semantic. The multilayered annotation of the corpus involves a XML-TEI encoding followed by a tokenization step where each token is univocally identified through a CTS urn notation and then associated to a part-of-speech and a lemma. The text is lexically and semantically annotated on the basis of a lexicon and a domain ontology, the former structuring the most relevant terms occurring in the text and the latter representing the domain entities of interest (e.g. people, places, etc.). Moreover, each entity is connected to linked and non linked resources, including DBpedia and VIAF. Finally, the results of the three layers of analysis are gathered and shown through interactive visualization and storytelling techniques. A demo version of the integrated architecture was developed
A Data-Driven Approach for Tag Refinement and Localization in Web Videos
Tagging of visual content is becoming more and more widespread as web-based
services and social networks have popularized tagging functionalities among
their users. These user-generated tags are used to ease browsing and
exploration of media collections, e.g. using tag clouds, or to retrieve
multimedia content. However, not all media are equally tagged by users. Using
the current systems is easy to tag a single photo, and even tagging a part of a
photo, like a face, has become common in sites like Flickr and Facebook. On the
other hand, tagging a video sequence is more complicated and time consuming, so
that users just tag the overall content of a video. In this paper we present a
method for automatic video annotation that increases the number of tags
originally provided by users, and localizes them temporally, associating tags
to keyframes. Our approach exploits collective knowledge embedded in
user-generated tags and web sources, and visual similarity of keyframes and
images uploaded to social sites like YouTube and Flickr, as well as web sources
like Google and Bing. Given a keyframe, our method is able to select on the fly
from these visual sources the training exemplars that should be the most
relevant for this test sample, and proceeds to transfer labels across similar
images. Compared to existing video tagging approaches that require training
classifiers for each tag, our system has few parameters, is easy to implement
and can deal with an open vocabulary scenario. We demonstrate the approach on
tag refinement and localization on DUT-WEBV, a large dataset of web videos, and
show state-of-the-art results.Comment: Preprint submitted to Computer Vision and Image Understanding (CVIU
Content-Based Video Retrieval in Historical Collections of the German Broadcasting Archive
The German Broadcasting Archive (DRA) maintains the cultural heritage of
radio and television broadcasts of the former German Democratic Republic (GDR).
The uniqueness and importance of the video material stimulates a large
scientific interest in the video content. In this paper, we present an
automatic video analysis and retrieval system for searching in historical
collections of GDR television recordings. It consists of video analysis
algorithms for shot boundary detection, concept classification, person
recognition, text recognition and similarity search. The performance of the
system is evaluated from a technical and an archival perspective on 2,500 hours
of GDR television recordings.Comment: TPDL 2016, Hannover, Germany. Final version is available at Springer
via DO
- …