5 research outputs found
Compare More Nuanced:Pairwise Alignment Bilinear Network For Few-shot Fine-grained Learning
The recognition ability of human beings is developed in a progressive way.
Usually, children learn to discriminate various objects from coarse to
fine-grained with limited supervision. Inspired by this learning process, we
propose a simple yet effective model for the Few-Shot Fine-Grained (FSFG)
recognition, which tries to tackle the challenging fine-grained recognition
task using meta-learning. The proposed method, named Pairwise Alignment
Bilinear Network (PABN), is an end-to-end deep neural network. Unlike
traditional deep bilinear networks for fine-grained classification, which adopt
the self-bilinear pooling to capture the subtle features of images, the
proposed model uses a novel pairwise bilinear pooling to compare the nuanced
differences between base images and query images for learning a deep distance
metric. In order to match base image features with query image features, we
design feature alignment losses before the proposed pairwise bilinear pooling.
Experiment results on four fine-grained classification datasets and one generic
few-shot dataset demonstrate that the proposed model outperforms both the
state-ofthe-art few-shot fine-grained and general few-shot methods.Comment: ICME 2019 Ora
Learning from Very Few Samples: A Survey
Few sample learning (FSL) is significant and challenging in the field of
machine learning. The capability of learning and generalizing from very few
samples successfully is a noticeable demarcation separating artificial
intelligence and human intelligence since humans can readily establish their
cognition to novelty from just a single or a handful of examples whereas
machine learning algorithms typically entail hundreds or thousands of
supervised samples to guarantee generalization ability. Despite the long
history dated back to the early 2000s and the widespread attention in recent
years with booming deep learning technologies, little surveys or reviews for
FSL are available until now. In this context, we extensively review 300+ papers
of FSL spanning from the 2000s to 2019 and provide a timely and comprehensive
survey for FSL. In this survey, we review the evolution history as well as the
current progress on FSL, categorize FSL approaches into the generative model
based and discriminative model based kinds in principle, and emphasize
particularly on the meta learning based FSL approaches. We also summarize
several recently emerging extensional topics of FSL and review the latest
advances on these topics. Furthermore, we highlight the important FSL
applications covering many research hotspots in computer vision, natural
language processing, audio and speech, reinforcement learning and robotic, data
analysis, etc. Finally, we conclude the survey with a discussion on promising
trends in the hope of providing guidance and insights to follow-up researches.Comment: 30 page