[visionlist] Deadline Extended - ICCV Workshop in Video Retrieval Methods and Their Limits (ViRaL)

[Asad Anwar Butt]

ViRaL19: 1st International Workshop on Video Retrieval Methods and Their
Limits
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@ International Conference on Computer Vision (ICCV)
Seoul, South Korea, October 27-November 2, 2019

Final Call for Papers
Conference website: https://sites.google.com/view/viral2019
Submission link: https://easychair.org/conferences/?conf=viral19
Submission deadline: *EXTENDED TO AUGUST 4, 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:

- Internet archives collection (IACC.3), which contains 600 hours of video,
90 ad-hoc queries and available ground truth.
- 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.
- 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".

All submissions will be handled electronically via EasyChair :
easychair.org/conferences/?conf=viral19

Important Dates

Workshop paper submission deadline : August 4, 2019 (extended)
Notification to authors : August 22, 2019
Workshop camera-ready submission : August 30, 2019
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