13,902 research outputs found
Content specific feature learning for fine-grained plant classification
We present the plant classification system submitted by the QUT RV team to the LifeCLEF 2015 plant task. Our system learns a content speciffic feature for various plant parts such as branch, leaf, fruit, ower and stem. These features are learned using a deep convolutional neural network. Experiments on the LifeCLEF 2015 plant dataset show that the proposed method achieves good performance with a score of 0:633 on the test set.</p
Integrating Semantic Knowledge to Tackle Zero-shot Text Classification
Insufficient or even unavailable training data of emerging classes is a big
challenge of many classification tasks, including text classification.
Recognising text documents of classes that have never been seen in the learning
stage, so-called zero-shot text classification, is therefore difficult and only
limited previous works tackled this problem. In this paper, we propose a
two-phase framework together with data augmentation and feature augmentation to
solve this problem. Four kinds of semantic knowledge (word embeddings, class
descriptions, class hierarchy, and a general knowledge graph) are incorporated
into the proposed framework to deal with instances of unseen classes
effectively. Experimental results show that each and the combination of the two
phases achieve the best overall accuracy compared with baselines and recent
approaches in classifying real-world texts under the zero-shot scenario.Comment: Accepted NAACL-HLT 201
Fine-grained Categorization and Dataset Bootstrapping using Deep Metric Learning with Humans in the Loop
Existing fine-grained visual categorization methods often suffer from three
challenges: lack of training data, large number of fine-grained categories, and
high intraclass vs. low inter-class variance. In this work we propose a generic
iterative framework for fine-grained categorization and dataset bootstrapping
that handles these three challenges. Using deep metric learning with humans in
the loop, we learn a low dimensional feature embedding with anchor points on
manifolds for each category. These anchor points capture intra-class variances
and remain discriminative between classes. In each round, images with high
confidence scores from our model are sent to humans for labeling. By comparing
with exemplar images, labelers mark each candidate image as either a "true
positive" or a "false positive". True positives are added into our current
dataset and false positives are regarded as "hard negatives" for our metric
learning model. Then the model is retrained with an expanded dataset and hard
negatives for the next round. To demonstrate the effectiveness of the proposed
framework, we bootstrap a fine-grained flower dataset with 620 categories from
Instagram images. The proposed deep metric learning scheme is evaluated on both
our dataset and the CUB-200-2001 Birds dataset. Experimental evaluations show
significant performance gain using dataset bootstrapping and demonstrate
state-of-the-art results achieved by the proposed deep metric learning methods.Comment: 10 pages, 9 figures, CVPR 201
Fine-Grained Object Recognition and Zero-Shot Learning in Remote Sensing Imagery
Fine-grained object recognition that aims to identify the type of an object
among a large number of subcategories is an emerging application with the
increasing resolution that exposes new details in image data. Traditional fully
supervised algorithms fail to handle this problem where there is low
between-class variance and high within-class variance for the classes of
interest with small sample sizes. We study an even more extreme scenario named
zero-shot learning (ZSL) in which no training example exists for some of the
classes. ZSL aims to build a recognition model for new unseen categories by
relating them to seen classes that were previously learned. We establish this
relation by learning a compatibility function between image features extracted
via a convolutional neural network and auxiliary information that describes the
semantics of the classes of interest by using training samples from the seen
classes. Then, we show how knowledge transfer can be performed for the unseen
classes by maximizing this function during inference. We introduce a new data
set that contains 40 different types of street trees in 1-ft spatial resolution
aerial data, and evaluate the performance of this model with manually annotated
attributes, a natural language model, and a scientific taxonomy as auxiliary
information. The experiments show that the proposed model achieves 14.3%
recognition accuracy for the classes with no training examples, which is
significantly better than a random guess accuracy of 6.3% for 16 test classes,
and three other ZSL algorithms.Comment: G. Sumbul, R. G. Cinbis, S. Aksoy, "Fine-Grained Object Recognition
and Zero-Shot Learning in Remote Sensing Imagery", IEEE Transactions on
Geoscience and Remote Sensing (TGRS), in press, 201
The Devil is in the Tails: Fine-grained Classification in the Wild
The world is long-tailed. What does this mean for computer vision and visual
recognition? The main two implications are (1) the number of categories we need
to consider in applications can be very large, and (2) the number of training
examples for most categories can be very small. Current visual recognition
algorithms have achieved excellent classification accuracy. However, they
require many training examples to reach peak performance, which suggests that
long-tailed distributions will not be dealt with well. We analyze this question
in the context of eBird, a large fine-grained classification dataset, and a
state-of-the-art deep network classification algorithm. We find that (a) peak
classification performance on well-represented categories is excellent, (b)
given enough data, classification performance suffers only minimally from an
increase in the number of classes, (c) classification performance decays
precipitously as the number of training examples decreases, (d) surprisingly,
transfer learning is virtually absent in current methods. Our findings suggest
that our community should come to grips with the question of long tails
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