2,377 research outputs found
Zero-Shot Learning from Adversarial Feature Residual to Compact Visual Feature
Recently, many zero-shot learning (ZSL) methods focused on learning
discriminative object features in an embedding feature space, however, the
distributions of the unseen-class features learned by these methods are prone
to be partly overlapped, resulting in inaccurate object recognition. Addressing
this problem, we propose a novel adversarial network to synthesize compact
semantic visual features for ZSL, consisting of a residual generator, a
prototype predictor, and a discriminator. The residual generator is to generate
the visual feature residual, which is integrated with a visual prototype
predicted via the prototype predictor for synthesizing the visual feature. The
discriminator is to distinguish the synthetic visual features from the real
ones extracted from an existing categorization CNN. Since the generated
residuals are generally numerically much smaller than the distances among all
the prototypes, the distributions of the unseen-class features synthesized by
the proposed network are less overlapped. In addition, considering that the
visual features from categorization CNNs are generally inconsistent with their
semantic features, a simple feature selection strategy is introduced for
extracting more compact semantic visual features. Extensive experimental
results on six benchmark datasets demonstrate that our method could achieve a
significantly better performance than existing state-of-the-art methods by
1.2-13.2% in most cases
Semantic Embedding Space for Zero-Shot Action Recognition
The number of categories for action recognition is growing rapidly. It is
thus becoming increasingly hard to collect sufficient training data to learn
conventional models for each category. This issue may be ameliorated by the
increasingly popular 'zero-shot learning' (ZSL) paradigm. In this framework a
mapping is constructed between visual features and a human interpretable
semantic description of each category, allowing categories to be recognised in
the absence of any training data. Existing ZSL studies focus primarily on image
data, and attribute-based semantic representations. In this paper, we address
zero-shot recognition in contemporary video action recognition tasks, using
semantic word vector space as the common space to embed videos and category
labels. This is more challenging because the mapping between the semantic space
and space-time features of videos containing complex actions is more complex
and harder to learn. We demonstrate that a simple self-training and data
augmentation strategy can significantly improve the efficacy of this mapping.
Experiments on human action datasets including HMDB51 and UCF101 demonstrate
that our approach achieves the state-of-the-art zero-shot action recognition
performance.Comment: 5 page
Ontology-based Classification of News in an Electronic Newspaper
This paper deals with the classification of news items in ePaper, a prototype system of a future
personalized newspaper service on a mobile reading device. The ePaper system aggregates news items from
various news providers and delivers to each subscribed user (reader) a personalized electronic newspaper,
utilizing content-based and collaborative filtering methods. The ePaper can also provide users "standard" (i.e., not
personalized) editions of selected newspapers, as well as browsing capabilities in the repository of news items.
This paper concentrates on the automatic classification of incoming news using hierarchical news ontology.
Based on this classification on one hand, and on the users' profiles on the other hand, the personalization engine
of the system is able to provide a personalized paper to each user onto her mobile reading device
Pairwise Instance Relation Augmentation for Long-tailed Multi-label Text Classification
Multi-label text classification (MLTC) is one of the key tasks in natural
language processing. It aims to assign multiple target labels to one document.
Due to the uneven popularity of labels, the number of documents per label
follows a long-tailed distribution in most cases. It is much more challenging
to learn classifiers for data-scarce tail labels than for data-rich head
labels. The main reason is that head labels usually have sufficient
information, e.g., a large intra-class diversity, while tail labels do not. In
response, we propose a Pairwise Instance Relation Augmentation Network (PIRAN)
to augment tailed-label documents for balancing tail labels and head labels.
PIRAN consists of a relation collector and an instance generator. The former
aims to extract the document pairwise relations from head labels. Taking these
relations as perturbations, the latter tries to generate new document instances
in high-level feature space around the limited given tailed-label instances.
Meanwhile, two regularizers (diversity and consistency) are designed to
constrain the generation process. The consistency-regularizer encourages the
variance of tail labels to be close to head labels and further balances the
whole datasets. And diversity-regularizer makes sure the generated instances
have diversity and avoids generating redundant instances. Extensive
experimental results on three benchmark datasets demonstrate that PIRAN
consistently outperforms the SOTA methods, and dramatically improves the
performance of tail labels
- …