Paper Track: Submission Instructions

We welcome paper submissions up to 4 pages (excluding references or supplementary materials). Please submit at the
VLAR 2023 @ ICCV 2023 CMT website. 

The paper submissions must be in pdf format and use the official ICCV 2023 templates. All submissions must be anonymous and conform to ICCV standards for double-blind review. The deadline for submitting the (optional) supplementary material is the same as the main paper deadline. The (optional) supplementary material should be submitted as a separate file from the main paper. The accepted papers will be included in the ICCV 2023 proceedings. The 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. The presentation of the accepted papers at VLAR 2023 will follow the same policy as that for the accepted papers of ICCV 2023.  

Submission deadline (both main paper & (optional) supplementary material): July 20 24, 2023 (11:59PM EDT). 
Notification to authors: August 7, 2023.
Camera ready deadline:  August 21, 2023 (AoE time).

We invite the submission of original and high-quality research papers in the topics related to vision-and-language algorithmic reasoning. Accepted work will be presented as either an oral, spotlight, or poster presentation. 

Paper Track: Topics

The topics for VLAR 2023 include, but are not limited to:

• Large language models, vision, and cognition including children’s cognition.
• 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 visual-language reasoning, e.g., using sketches, diagrams, etc.
• Perceptual reasoning and decision making.
• Multimodal cognition and learning.
• New vision-and-language abstract reasoning tasks and datasets.
• Vision-and-language applications.

SMART-101 Challenge Track: Participation Instructions

As part of VLAR 2023, we are hosting a challenge based on the Simple Multimodal Algorithmic Reasoning Task – SMART-101 – dataset. The accompanying CVPR 2023 paper is: “Are Deep Neural Networks SMARTer than Second Graders?” The challenge will be hosted on Eval AI. The challenge participants are required to make arXiv submissions detailing their approach. These are only used to judge the competition, and will not be reviewed and will not be part of workshop proceedings. The winners of the challenge are determined both by performance on the leaderboard over a private test set as well as the novelty of the proposed method (as detailed in the arXiv submission). The details will be made available on the challenge website. The prizes will be awarded to the winners on the day of the workshop. The key dates of the challenge are as follows:  

Challenge open: June 15, 2023. 
Challenge details: SMART-101 Challenge page on Eval AI. 
Submission deadline and arXiv paper deadline to be considered for awards: September 1 15, 2023 (11:59PM EDT).
Public winner announcement:  October 3, 2023 (11:59PM EDT). 

Oral Papers

• Uni-NLX: Unifying Textual Explanations for Vision and Vision-Language Tasks.
  Fawaz Sammani, Nikos Deligiannis.


• SelfGraphVQA: A Self-Supervised Graph Neural Network for Scene-based Question Answering.
  Bruno Cesar de Oliveira Souza, Marius Aasan, Helio Pedrini, Adín Ramírez Rivera.


• Iterative Robust Visual Grounding with Masked Reference based Centerpoint Supervision.
  Menghao Li, Chunlei Wang, Wenquan Feng, Shuchang Lyu, Guangliang Cheng, Xiangtai Li, Binghao Liu, Qi Zhao.


• Pointing out Human Answer Mistakes in a Goal-Oriented Visual Dialogue.
  Ryosuke Oshima, Seitaro Shinagawa, Hideki Tsunashima, Qi Feng, Shigeo Morishima.

Spotlight Papers

• What If the TV Was Off? Examining Counterfactual Reasoning Abilities of Multi-modal Language Models.
  Letian Zhang, Xiaotong Zhai, Zhongkai Zhao, Xin Wen, Bingchen Zhao.


• Understanding Video Scenes through Text: Insights from Text-based Video Question Answering.
  Soumya Shamarao Jahagirdar, Minesh Mathew, Dimosthenis Karatzas, C.V. Jawahar.


• MMTF: Multi-Modal Temporal Fusion for Commonsense Video Question Answering.
  Mobeen Ahmad, Geonwoo Park, Sang Uk Park.


• VQA Therapy: Exploring Answer Differences by Visually Grounding Answers.
  Chongyan Chen, Samreen Anjum, Danna Gurari.


• Language-enhanced RNR-Map: Querying Renderable Neural Radiance Field Maps with Natural Language.
  Francesco Taioli, Federico Cunico, Federico Girella, Riccardo Bologna, Alessandro Farinelli, Marco Cristani.


• CLIP-Decoder: ZeroShot Multilabel Classification using Multimodal CLIP Aligned Representations.
  Muhammad Ali, Salman Khan.