[visionlist] 2nd CFP: ICCV Workshop in Video Retrieval Methods and Their Limits (ViRaL)

[Asad Anwar Butt]

*Second Call for Papers*
*1st International Workshop on Video Retrieval Methods and Their Limits*
*In conjunction with ICCV 2019*

With the vastly increasing amount of video data being created, searching in
video is a common task in many application areas, such as media &
entertainment, surveillance or medicine. The success of video search relies
crucially on indexing video content, which is often done based on textual
information, after extracting text or adding labels based on detection or
classification of the visual or audio content. Video search systems are
thus often built by integrating a set analysis components, many of which
rely on computer vision algorithms, and fusing their results to create an
efficiently searchable index.

This has the consequence that the performance of video search & retrieval
systems is impacted by many factors, which makes the analysis of which
components of the system contribute to the success or failure in a
particular case difficult. The fact that many of the components have moved
to deep neural network (DNN) based approaches in recent years has not made
this analysis easier. Benchmarking initiatives for video analysis and
retrieval, such as TRECVID, have significantly contributed to a more
systematic evaluation and have tremendously fostered the evolution of
systems. However, their results show that there are usually outliers in the
performance of a system on specific queries or datasets. In the existing
literature, these aspects of comparative analysis and failure analysis are
not sufficiently explored.

The 1st international workshop on video retrieval methods and their limits
is calling for contributions in video search using different types of
queries. For example, searching within videos can be of two types:

   - *General search* (also known as ad-hoc search) uses natural language
   queries (and possibly image/video queries), which are used by systems to
   retrieve relevant video sequences. Queries may specify certain conditions
   that must be satisfied for a video to be considered relevant.
   - *Instance search* requires the retrieval of specific objects, persons,
   location, or a combination of these entities given an example image(s) of
   the target(s) of interest.

In this context, contributions related (but not limited) to the following
topics are invited.

   - Comparative analysis of performance of search systems on different
   datasets
   - Fusion of computer vision, text/language processing and audio analysis
   for video search
   - Evaluation protocols and metrics for assessing the impact of specific
   components of retrieval systems
   - Failure analysis of vision-based components in video search and
   retrieval systems
   - Failure analysis of query types, dataset characteristics, metrics, and
   system architectures
   - Integrating user interaction in search systems and their impact on
   performance
   - Approaches for measuring and predicting hardness/complexity of queries
   in a system-independent way

Interested authors are invited to apply their approaches and methods on the
existing datasets prepared by the workshop organizers. These include:

   1. Internet archives collection (IACC.3), which contains 600 hours of
   video, 90 ad-hoc queries and available ground truth.
   2. BBC Eastenders dataset contains episodes of the weekly show over a
   period of 5 years. This amounts to 464 hours of video, and has available
   177 instance search queries and the ground truth.
   3. The new V3C1 Vimeo internet collection contains 1000 hours of video
   and will be used at the annual TRECVID international content-based video
   retrieval evaluation benchmark starting in 2019.

However, any external datasets can also be used. Failure analysis of system
performance are highly encouraged and will be given high priority with the
goal to identify which methods works and which don’t, and why. Examples of
such failure modes include, but are not limited to: easy vs hard queries,
dataset characteristics, training data characteristics and its effect on
solving easy/hard queries, system architecture (e.g NN depth and
attributes).

*Submission*
We invite papers of up to 4 pages length (excluding references, but
including figures), formatted according to the ICCV template (
http://iccv2019.thecvf.com/files/iccv2019AuthorKit.zip). Submissions shall
be single blind, i.e. do not need to be anonymized.

By submitting a manuscript to ICCV, authors acknowledge that it has not
been previously published or accepted for publication in substantially
similar form in any peer-reviewed venue including journal, conference or
workshop. Furthermore, no publication substantially similar in content has
been or will be submitted to this or another conference, workshop, or
journal during the review period. A publication, for the purposes of this
policy, is defined to be a written work longer than four pages (excluding
references) that was submitted for review by peers for either acceptance or
rejection, and, after review, was accepted. In particular, this definition
of publication does not depend upon whether such an accepted written work
appears in a formal proceedings or whether the organizers declare that such
work “counts as a publication”.

*Workshop website: *https://sites.google.com/view/viral2019

*Submission link:  *  https://easychair.org/conferences/?conf=viral19


*Important Dates*

Workshop paper submission deadline: July 26, 2019
Notification to authors: August 22, 2019
Workshop camera-ready submission: August 30, 2019
Workshop: October 28, 2019

*Organizing Committee and Contacts:*

George Awad: george.awad at nist.gov
National Institute of Standards and Technology, USA

Werner Bailer:  werner.bailer at joanneum.at
Joanneum Research, Austria

Asad A. Butt: asad.butt at nist.gov
Johns Hopkins University; National Institute of Standards and Technology,
USA

Keith Curtis: keith.curtis at nist.gov
National Institute of Standards and Technology, USA

Luca Rossetto: luca.rossetto at unibas.ch
University of Basel, Switzerland

Klaus Schoeffmann: ks at itec.aau.at
Klagenfurt University, Austria

Ian Soboroff: ian.soboroff at nist.gov
National Institute of Standards and Technology, USA
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