[visionlist] [Extended Deadline: April 20th] IJCV Special Issue on Efficient Visual Recognition

[Jie Qin]

Apologies for cross-posting
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* International Journal of Computer Vision Special Issue on Efficient
Visual Recognition

* Website:
http://www.ee.oulu.fi/~lili/IJCVSIEVR2018.htm

* Date:
Submission of full papers: *April 20th, 2019 [NEW]*

* Guest Editors:
- Li Liu, National University of Defense Technology, China & University of
Oulu, Finland
- Matti Pietikäinen, University of Oulu, Finland
- Jie Qin, ETH Zürich, Switzerland
- Jie Chen, University of Oulu, Finland
- Wanli Ouyang, University of Sydney, Australia
- Luc Van Gool,  ETH Zürich, Switzerland

============================ Scope =================================
Visual recognition plays a central role in computer vision. A large amount
of vision tasks fundamentally rely on the ability to recognize and localize
faces, people, objects, scenes, places, attributes, actions and relations.
Visual recognition thus touches many areas of artificial intelligence and
information retrieval, such as image search, visual surveillance, video
data mining, question answering, autonomous driving and robotic
interactions.

Feature representation is the core of visual recognition. Milestone
handcrafted feature descriptors such as Scale Invariant Feature Transform
(SIFT), Speeded Up Robust Features (SURF), Histogram of Oriented Gradients
(HOG) and Local Binary Pattern (LBP) have dominated visual recognition for
years until the turning point in 2012 when Deep Convolutional Neural
Networks (DCNN) achieved a record-breaking image classification accuracy.
Since DCNN entered the scene, visual recognition has been experiencing a
revolution and tremendous progress (such as enabling superhuman accuracy)
has been achieved because of the availability of large visual datasets and
GPU computing resources. Hand in hand went, the development of deeper and
larger DCNNs that could automatically learn more and more powerful feature
representations with multiple levels of abstraction from big data.

In many real-world applications, recognizing efficiently is as critical as
recognizing accurately. Significant progress has been made in the past few
years to boost the accuracy levels of visual recognition, but existing
solutions often rely on computationally expensive feature representation
and learning approaches, which are too slow for numerous applications. In
addition to the opportunities they offer, the large visual datasets also
lead to the challenge of scaling up while retaining the efficiency of
learning approaches and representations for both handcrafted and deeply
learned features.

In addition, given sufficient amount of annotated visual data, some
existing features, especially DCNN features, have been shown to yield high
accuracy for visual recognition. However, there are many applications where
only limited amounts of annotated training data can be available or
collecting labeled training data is too expensive. Such applications impose
great challenges to many existing features. Finally, with the prevalence of
social media networks and mobile/wearable devices which have limited
computational capabilities and storage space, the demand for sophisticated
mobile/wearable device applications in handling visual big data recognition
are rising. In such applications, real-time performance is of utmost
importance to users, since no one is willing to spend time waiting
nowadays. Therefore, there is a growing need for developing visual features
that are fast to compute, memory efficient, and yet exhibiting good
discriminability and robustness for visual recognition.

============================ Topics ================================
We encourage researchers to study and develop novel efficient visual
recognition approaches that are computationally efficient, memory
efficient, and yet exhibiting good recognition accuracy. We aim to solicit
original contributions that: (1) present state of the art theories related
to efficient visual recognition; (2) explore novel algorithms and
applications; (3) survey the recent progress in this field; and (4)
establish benchmark datasets.

The list of possible topics includes, but is not limited to:
§ Hashing/binary coding and its related applications
§ Compact and efficient convolutional neural networks
§ Efficient handcrafted feature design
§ Fast features tailored to wearable/mobile devices
§ Efficient dimensionality reduction and feature selection
§ Sparse representation and its related applications
§ Evaluations of current handcrafted descriptors and deep learning based
features
§ DCNN compression/quantization/binarization
§ Hybrid methods combining strengths of handcrafted and learning based
approaches
§ Efficient feature learning for applications with limited amounts of
annotated training data
§ Efficient approaches to increase the invariance of DCNN

Priority will be given to papers with high novelty and originality for
research papers, and to papers with high potential impact for
survey/overview papers.

======================= Paper Submission and Review
===========================
Authors are encouraged to submit original work that has not appeared in,
nor is in consideration by, other journals. Papers extending previously
published conference papers can be submitted, as long as the journal
submission provides a significant contribution beyond the conference paper
(The overlap is described clearly at the beginning of the journal
submission).

Manuscripts will be subject to a peer-reviewing process and must conform to
the author guidelines available on the IJCV website at Instructions for
Authors on the right panel.

Authors need to submit full papers online through the IJCV submission site
at http://visi.edmgr.com, selecting the choice that indicates this special
issue Efficient Visual Recognition.

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We look forward to your contributions!

Best Regards,

Jie Qin

Research Scientist,
Inception Institute of Artificial Intelligence,
Abu Dhabi, United Arab Emirates

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