DeepVision: Temporal Deep Learning (TDL) Call for Papers

Call for paper
CVPR 2017 workshop on
DeepVision: Temporal Deep Learning (TDL)
26 July 2017, Honolulu, Hawaii, USA

The computer vision community over the past few years has been
dominated by deep learning based techniques. These techniques have,
however, been mostly focused on still images, although many new
publicly available data and high impact applications benefit from
video recordings. Videos contain valuable temporal information that
can be exploited to achieve better performance. Exploiting temporal
information is of great importance in computer vision applications,
like object tracking and recognition, scene analysis and
understanding, etc. Deep learning based techniques are challenged to
employ temporal information in such applications. Although some
advances have been performed in this direction, mainly involving 3D
convolutions, motion-based input features, or deep temporal- based
models such as RNN-LSTM, significant advances are expected to be
performed in this field. ?Papers on deep learning techniques
utilizing temporal information on any of the following topics can be
covered by the workshop:

TDL object recognition
TDL object tracking
TDL scene analysis
TDL shape analysis
TDL crowd analysis
TDL human body motion analysis
TDL facial analysis systems
New TDL models
New applications of TDL

The submitted papers are limited to eight pages, including figures and
tables, in the CVPR style. Additional pages containing only cited
references are allowed. CVPR guidelines and templates given in the
following page should be used:

The accepted papers will be presented as posters at deep vision
workshop and will be published in CVPR proceedings.

Important dates:

Submission deadline: March 10
Decision to authors: April 5
Camera ready: April 8
Submission through CMT at:

Organizing committee:
Kamal Nasrollahi (primary contact regarding paper submission:, Jose Alvarez Lopez, Sergio Escalera, Nathan
Silberman, Ajmal Mian, Dhruv Batra, Gholamreza Anbarjafari, Yann LeCun
, and Thomas B. Moeslund

Invited Speakers:
Gabriel Kreiman
Sanja Fidler
Raia Hadsell
Hugo Larochelle
Lior Wolf
Temporal Deep Learning:
Maja Pantic
Trevor Darrell
Xiaogang Wang