CVPR 2024 Multimodal Algorithmic Reasoning Workshop Call for Papers

Call for Papers: CVPR 2024 Multimodal Algorithmic Reasoning Workshop

The upcoming CVPR 2024 Multimodal Algorithmic Reasoning (MAR)
Workshop, to be held on June 17th, 2024, at the Seattle Convention
Center, is now accepting paper submissions.

In this workshop, we gather researchers working in neural algorithmic
learning, multimodal reasoning, and cognitive models of intelligence
to showcase their cutting-edge research, discuss the latest
challenges, as well as bring to the forefront problems in perception
and language modeling that are often overlooked but are pivotal in
achieving true artificial general intelligence. An emphasis of this
workshop is on the emerging topic of multimodal algorithmic reasoning,
where a reasoning agent is required to automatically deduce new
algorithms/procedures for solving real-world tasks, e.g., algorithms
that use multimodal foundational models for analysis, synthesis, and
planning, new approaches towards solving challenging
vision-and-language mathematical (Olympiad type) reasoning problems,
deriving winning strategies in multimodal games, procedures for using
tools in robotic manipulation, etc. We hope to deep dive into this
exciting topic at the intersection of multimodal learning and
cognitive science to understand what we have achieved thus far in
machine intelligence and what we are lacking in relation to the human
way of thinking -- through talks from outstanding researchers and
faculty that could inspire the audience to search for the missing
rungs on the ladder to true intelligence.



Submission deadline: ***March 11, 2024*** (11:59PM EDT) 

Paper decisions to authors: April 5, 2024

Camera-ready deadline: April 10, 2024 



We invite submissions of high-quality research papers in the topics
related to multimodal algorithmic reasoning. The topics for MAR 2024
include, but are not limited to:

* Multimodal Large language models

* Large language models and algorithmic reasoning

* Multimodal machine cognition and learning

* Foundation models of intelligence, including vision, language, and other modalities

* Artificial general intelligence / general-purpose problem solving architectures

* Neural architectures for solving vision & language or language-based IQ puzzles

* Embodiment and AI

* Large language models, neuroscience, and vision

* Functional and algorithmic / procedural learning in vision

* Abstract multimodal reasoning, e.g., using sketches, diagrams, etc.

* Perceptual reasoning and decision making

* New vision-and-language abstract reasoning tasks and datasets

* Vision-and-language applications



We have four tracks for paper submissions:

      1. Short papers with IEEE/CVF workshop proceedings (= 4 pages)

      2. Long papers with IEEE/CVF workshop proceedings (= 8 pages)

      3. Papers without proceedings (= 8 pages), and

      4. Previously published papers (= 8 pages).

For tracks 1-3, we are inviting only original, previously
unpublished papers, and dual submissions are not allowed. The page
limits described above are excluding the references. We plan to accept
only a limited number of previously accepted papers in track 4 if our
final program schedule allows. Please see the workshop website for
more details.

* All submissions are handled via the workshop’s CMT website:

* Submissions should be made in PDF format and should follow the
official CVPR 2024 template and guidelines.

* All submissions should maintain author anonymity and should abide by
the CVPR conference guidelines for double-blind review.

* Accepted papers will be presented as either an oral, spotlight, or
poster presentation. At least one author of each accepted submission
must present the paper at the workshop.

* Presentation of accepted papers at our workshop will follow the same
policy as that for accepted papers at the CVPR main conference

* Papers accepted in tracks 1-2 will be part of the CVPR 2024
workshop proceedings.

* Authors may optionally upload supplementary materials, the deadline
for which is the same as that of the main paper and should be
submitted separately.


Anoop Cherian, Mitsubishi Electric Research Laboratories

Suhas Lohit, Mitsubishi Electric Research Laboratories

Honglu Zhou, Salesforce Research

Moitreya Chatterjee, Mitsubishi Electric Research Laboratories

Kuan-Chuan Peng, Mitsubishi Electric Research Laboratories

Kevin A. Smith, Massachusetts Institute of Technology

Tim K. Marks, Mitsubishi Electric Research Laboratories

Joanna Matthiesen, Math Kangaroo USA

Joshua B. Tenenbaum, Massachusetts Institute of Technology




SMART-101 project: 

Website: h

Thank you,