68,345 research outputs found
Diversity Transfer Network for Few-Shot Learning
Few-shot learning is a challenging task that aims at training a classifier
for unseen classes with only a few training examples. The main difficulty of
few-shot learning lies in the lack of intra-class diversity within insufficient
training samples. To alleviate this problem, we propose a novel generative
framework, Diversity Transfer Network (DTN), that learns to transfer latent
diversities from known categories and composite them with support features to
generate diverse samples for novel categories in feature space. The learning
problem of the sample generation (i.e., diversity transfer) is solved via
minimizing an effective meta-classification loss in a single-stage network,
instead of the generative loss in previous works.
Besides, an organized auxiliary task co-training over known categories is
proposed to stabilize the meta-training process of DTN. We perform extensive
experiments and ablation studies on three datasets, i.e., \emph{mini}ImageNet,
CIFAR100 and CUB. The results show that DTN, with single-stage training and
faster convergence speed, obtains the state-of-the-art results among the
feature generation based few-shot learning methods. Code and supplementary
material are available at: \texttt{https://github.com/Yuxin-CV/DTN}Comment: 9 pages, 3 figures, AAAI 202
Automatic Discovery, Association Estimation and Learning of Semantic Attributes for a Thousand Categories
Attribute-based recognition models, due to their impressive performance and
their ability to generalize well on novel categories, have been widely adopted
for many computer vision applications. However, usually both the attribute
vocabulary and the class-attribute associations have to be provided manually by
domain experts or large number of annotators. This is very costly and not
necessarily optimal regarding recognition performance, and most importantly, it
limits the applicability of attribute-based models to large scale data sets. To
tackle this problem, we propose an end-to-end unsupervised attribute learning
approach. We utilize online text corpora to automatically discover a salient
and discriminative vocabulary that correlates well with the human concept of
semantic attributes. Moreover, we propose a deep convolutional model to
optimize class-attribute associations with a linguistic prior that accounts for
noise and missing data in text. In a thorough evaluation on ImageNet, we
demonstrate that our model is able to efficiently discover and learn semantic
attributes at a large scale. Furthermore, we demonstrate that our model
outperforms the state-of-the-art in zero-shot learning on three data sets:
ImageNet, Animals with Attributes and aPascal/aYahoo. Finally, we enable
attribute-based learning on ImageNet and will share the attributes and
associations for future research.Comment: Accepted as a conference paper at CVPR 201
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