1,567 research outputs found
Exemplar Based Deep Discriminative and Shareable Feature Learning for Scene Image Classification
In order to encode the class correlation and class specific information in
image representation, we propose a new local feature learning approach named
Deep Discriminative and Shareable Feature Learning (DDSFL). DDSFL aims to
hierarchically learn feature transformation filter banks to transform raw pixel
image patches to features. The learned filter banks are expected to: (1) encode
common visual patterns of a flexible number of categories; (2) encode
discriminative information; and (3) hierarchically extract patterns at
different visual levels. Particularly, in each single layer of DDSFL, shareable
filters are jointly learned for classes which share the similar patterns.
Discriminative power of the filters is achieved by enforcing the features from
the same category to be close, while features from different categories to be
far away from each other. Furthermore, we also propose two exemplar selection
methods to iteratively select training data for more efficient and effective
learning. Based on the experimental results, DDSFL can achieve very promising
performance, and it also shows great complementary effect to the
state-of-the-art Caffe features.Comment: Pattern Recognition, Elsevier, 201
Self-tuned Visual Subclass Learning with Shared Samples An Incremental Approach
Computer vision tasks are traditionally defined and evaluated using semantic
categories. However, it is known to the field that semantic classes do not
necessarily correspond to a unique visual class (e.g. inside and outside of a
car). Furthermore, many of the feasible learning techniques at hand cannot
model a visual class which appears consistent to the human eye. These problems
have motivated the use of 1) Unsupervised or supervised clustering as a
preprocessing step to identify the visual subclasses to be used in a
mixture-of-experts learning regime. 2) Felzenszwalb et al. part model and other
works model mixture assignment with latent variables which is optimized during
learning 3) Highly non-linear classifiers which are inherently capable of
modelling multi-modal input space but are inefficient at the test time. In this
work, we promote an incremental view over the recognition of semantic classes
with varied appearances. We propose an optimization technique which
incrementally finds maximal visual subclasses in a regularized risk
minimization framework. Our proposed approach unifies the clustering and
classification steps in a single algorithm. The importance of this approach is
its compliance with the classification via the fact that it does not need to
know about the number of clusters, the representation and similarity measures
used in pre-processing clustering methods a priori. Following this approach we
show both qualitatively and quantitatively significant results. We show that
the visual subclasses demonstrate a long tail distribution. Finally, we show
that state of the art object detection methods (e.g. DPM) are unable to use the
tails of this distribution comprising 50\% of the training samples. In fact we
show that DPM performance slightly increases on average by the removal of this
half of the data.Comment: Updated ICCV 2013 submissio
Class-Agnostic Counting
Nearly all existing counting methods are designed for a specific object
class. Our work, however, aims to create a counting model able to count any
class of object. To achieve this goal, we formulate counting as a matching
problem, enabling us to exploit the image self-similarity property that
naturally exists in object counting problems. We make the following three
contributions: first, a Generic Matching Network (GMN) architecture that can
potentially count any object in a class-agnostic manner; second, by
reformulating the counting problem as one of matching objects, we can take
advantage of the abundance of video data labeled for tracking, which contains
natural repetitions suitable for training a counting model. Such data enables
us to train the GMN. Third, to customize the GMN to different user
requirements, an adapter module is used to specialize the model with minimal
effort, i.e. using a few labeled examples, and adapting only a small fraction
of the trained parameters. This is a form of few-shot learning, which is
practical for domains where labels are limited due to requiring expert
knowledge (e.g. microbiology). We demonstrate the flexibility of our method on
a diverse set of existing counting benchmarks: specifically cells, cars, and
human crowds. The model achieves competitive performance on cell and crowd
counting datasets, and surpasses the state-of-the-art on the car dataset using
only three training images. When training on the entire dataset, the proposed
method outperforms all previous methods by a large margin.Comment: Asian Conference on Computer Vision (ACCV), 201
Taking the bite out of automated naming of characters in TV video
We investigate the problem of automatically labelling appearances of characters in TV or film material
with their names. This is tremendously challenging due to the huge variation in imaged appearance of each character and the weakness and ambiguity of available annotation. However, we demonstrate that high precision can be achieved by combining multiple sources of information, both visual and textual. The principal novelties that we introduce are: (i) automatic generation of time stamped character annotation by aligning subtitles and transcripts; (ii) strengthening the supervisory information by identifying
when characters are speaking. In addition, we incorporate complementary cues of face matching and clothing matching to propose common annotations for face tracks, and consider choices of classifier which can potentially correct errors made in the automatic extraction of training data from the weak textual annotation. Results are presented on episodes of the TV series ‘‘Buffy the Vampire Slayer”
Incorporating Intra-Class Variance to Fine-Grained Visual Recognition
Fine-grained visual recognition aims to capture discriminative
characteristics amongst visually similar categories. The state-of-the-art
research work has significantly improved the fine-grained recognition
performance by deep metric learning using triplet network. However, the impact
of intra-category variance on the performance of recognition and robust feature
representation has not been well studied. In this paper, we propose to leverage
intra-class variance in metric learning of triplet network to improve the
performance of fine-grained recognition. Through partitioning training images
within each category into a few groups, we form the triplet samples across
different categories as well as different groups, which is called Group
Sensitive TRiplet Sampling (GS-TRS). Accordingly, the triplet loss function is
strengthened by incorporating intra-class variance with GS-TRS, which may
contribute to the optimization objective of triplet network. Extensive
experiments over benchmark datasets CompCar and VehicleID show that the
proposed GS-TRS has significantly outperformed state-of-the-art approaches in
both classification and retrieval tasks.Comment: 6 pages, 5 figure
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