3,724 research outputs found
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}
Combining visual recognition and computational linguistics : linguistic knowledge for visual recognition and natural language descriptions of visual content
Extensive efforts are being made to improve visual recognition and semantic understanding of language. However, surprisingly little has been done to exploit the mutual benefits of combining both fields. In this thesis we show how the different fields of research can profit from each other.
First, we scale recognition to 200 unseen object classes and show how to extract robust semantic relatedness from linguistic resources. Our novel approach extends zero-shot to few shot recognition and exploits unlabeled data by adopting label propagation for transfer learning.
Second, we capture the high variability but low availability of composite activity videos by extracting the essential information from text descriptions. For this we recorded and annotated a corpus for fine-grained activity recognition. We show improvements in a supervised case but we are also able to recognize unseen composite activities.
Third, we present a corpus of videos and aligned descriptions. We use it for grounding activity descriptions and for learning how to automatically generate natural language descriptions for a video. We show that our proposed approach is also applicable to image description and that it outperforms baselines and related work.
In summary, this thesis presents a novel approach for automatic video description and shows the benefits of extracting linguistic knowledge for object and activity recognition as well as the advantage of visual recognition for understanding activity descriptions.Trotz umfangreicher Anstrengungen zur Verbesserung der die visuelle Erkennung und dem automatischen Verständnis von Sprache, ist bisher wenig getan worden, um diese beiden Forschungsbereiche zu kombinieren. In dieser Dissertation zeigen wir, wie beide voneinander profitieren können.
Als erstes skalieren wir Objekterkennung zu 200 ungesehen Klassen und zeigen, wie man robust semantische Ähnlichkeiten von Sprachressourcen extrahiert. Unser neuer Ansatz kombiniert Transfer und halbüberwachten Lernverfahren und kann so Daten ohne Annotation ausnutzen und mit keinen als auch mit wenigen Trainingsbeispielen auskommen.
Zweitens erfassen wir die hohe Variabilität aber geringe Verfügbarkeit von Videos mit zusammengesetzten Aktivitäten durch Extraktion der wesentlichen Informationen aus Textbeschreibungen. Wir verbessern überwachtes Training als auch die Erkennung von ungesehenen Aktivitäten.
Drittens stellen wir einen parallelen Datensatz von Videos und Beschreibungen vor. Wir verwenden ihn für Grounding von Aktivitätsbeschreibungen und um die automatische Generierung natürlicher Sprache für ein Video zu erlernen. Wir zeigen, dass sich unsere Ansatz auch für Bildbeschreibung einsetzten lässt und das er bisherige Ansätze übertrifft.
Zusammenfassend stellt die Dissertation einen neuen Ansatz zur automatische Videobeschreibung vor und zeigt die Vorteile von sprachbasierten Ähnlichkeitsmaßen für die Objekt- und Aktivitätserkennung als auch umgekehrt
Visual Transfer Learning: Informal Introduction and Literature Overview
Transfer learning techniques are important to handle small training sets and
to allow for quick generalization even from only a few examples. The following
paper is the introduction as well as the literature overview part of my thesis
related to the topic of transfer learning for visual recognition problems.Comment: part of my PhD thesi
SimLex-999: Evaluating Semantic Models with (Genuine) Similarity Estimation
We present SimLex-999, a gold standard resource for evaluating distributional
semantic models that improves on existing resources in several important ways.
First, in contrast to gold standards such as WordSim-353 and MEN, it explicitly
quantifies similarity rather than association or relatedness, so that pairs of
entities that are associated but not actually similar [Freud, psychology] have
a low rating. We show that, via this focus on similarity, SimLex-999
incentivizes the development of models with a different, and arguably wider
range of applications than those which reflect conceptual association. Second,
SimLex-999 contains a range of concrete and abstract adjective, noun and verb
pairs, together with an independent rating of concreteness and (free)
association strength for each pair. This diversity enables fine-grained
analyses of the performance of models on concepts of different types, and
consequently greater insight into how architectures can be improved. Further,
unlike existing gold standard evaluations, for which automatic approaches have
reached or surpassed the inter-annotator agreement ceiling, state-of-the-art
models perform well below this ceiling on SimLex-999. There is therefore plenty
of scope for SimLex-999 to quantify future improvements to distributional
semantic models, guiding the development of the next generation of
representation-learning architectures
Multimodal Grounding for Language Processing
This survey discusses how recent developments in multimodal processing
facilitate conceptual grounding of language. We categorize the information flow
in multimodal processing with respect to cognitive models of human information
processing and analyze different methods for combining multimodal
representations. Based on this methodological inventory, we discuss the benefit
of multimodal grounding for a variety of language processing tasks and the
challenges that arise. We particularly focus on multimodal grounding of verbs
which play a crucial role for the compositional power of language.Comment: The paper has been published in the Proceedings of the 27 Conference
of Computational Linguistics. Please refer to this version for citations:
https://www.aclweb.org/anthology/papers/C/C18/C18-1197
Visually Grounded Meaning Representations
In this paper we address the problem of grounding distributional representations of lexical meaning. We introduce a new
model which uses stacked autoencoders to learn higher-level representations from textual and visual input. The visual modality is
encoded via vectors of attributes obtained automatically from images. We create a new large-scale taxonomy of 600 visual attributes
representing more than 500 concepts and 700K images. We use this dataset to train attribute classifiers and integrate their predictions
with text-based distributional models of word meaning. We evaluate our model on its ability to simulate word similarity judgments and
concept categorization. On both tasks, our model yields a better fit to behavioral data compared to baselines and related models which
either rely on a single modality or do not make use of attribute-based input
Combining Language and Vision with a Multimodal Skip-gram Model
We extend the SKIP-GRAM model of Mikolov et al. (2013a) by taking visual
information into account. Like SKIP-GRAM, our multimodal models (MMSKIP-GRAM)
build vector-based word representations by learning to predict linguistic
contexts in text corpora. However, for a restricted set of words, the models
are also exposed to visual representations of the objects they denote
(extracted from natural images), and must predict linguistic and visual
features jointly. The MMSKIP-GRAM models achieve good performance on a variety
of semantic benchmarks. Moreover, since they propagate visual information to
all words, we use them to improve image labeling and retrieval in the zero-shot
setup, where the test concepts are never seen during model training. Finally,
the MMSKIP-GRAM models discover intriguing visual properties of abstract words,
paving the way to realistic implementations of embodied theories of meaning.Comment: accepted at NAACL 2015, camera ready version, 11 page
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