10,360 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}
Fine-grained Image Classification by Exploring Bipartite-Graph Labels
Given a food image, can a fine-grained object recognition engine tell "which
restaurant which dish" the food belongs to? Such ultra-fine grained image
recognition is the key for many applications like search by images, but it is
very challenging because it needs to discern subtle difference between classes
while dealing with the scarcity of training data. Fortunately, the ultra-fine
granularity naturally brings rich relationships among object classes. This
paper proposes a novel approach to exploit the rich relationships through
bipartite-graph labels (BGL). We show how to model BGL in an overall
convolutional neural networks and the resulting system can be optimized through
back-propagation. We also show that it is computationally efficient in
inference thanks to the bipartite structure. To facilitate the study, we
construct a new food benchmark dataset, which consists of 37,885 food images
collected from 6 restaurants and totally 975 menus. Experimental results on
this new food and three other datasets demonstrates BGL advances previous works
in fine-grained object recognition. An online demo is available at
http://www.f-zhou.com/fg_demo/
Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly
Due to the importance of zero-shot learning, i.e. classifying images where
there is a lack of labeled training data, the number of proposed approaches has
recently increased steadily. We argue that it is time to take a step back and
to analyze the status quo of the area. The purpose of this paper is three-fold.
First, given the fact that there is no agreed upon zero-shot learning
benchmark, we first define a new benchmark by unifying both the evaluation
protocols and data splits of publicly available datasets used for this task.
This is an important contribution as published results are often not comparable
and sometimes even flawed due to, e.g. pre-training on zero-shot test classes.
Moreover, we propose a new zero-shot learning dataset, the Animals with
Attributes 2 (AWA2) dataset which we make publicly available both in terms of
image features and the images themselves. Second, we compare and analyze a
significant number of the state-of-the-art methods in depth, both in the
classic zero-shot setting but also in the more realistic generalized zero-shot
setting. Finally, we discuss in detail the limitations of the current status of
the area which can be taken as a basis for advancing it.Comment: Accepted by TPAMI in July, 2018. We introduce Proposed Split Version
2.0 (Please download it from our project webpage). arXiv admin note:
substantial text overlap with arXiv:1703.0439
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
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