# ViTCN: Vision Transformer Contrastive Network For Reasoning

arXiv: https://arxiv.org/abs/2403.09962 · HTML: https://mlsanity.com/paper/2403.09962/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 14 of its 18 references that are in the map.
- First posted on arXiv 15 Mar 2024.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [RAVEN: A Dataset for Relational and Analogical Visual rEasoNing](https://mlsanity.com/paper/1903.02741/) (2019)
- [Measuring abstract reasoning in neural networks](https://mlsanity.com/paper/1807.04225/) (2018)
- [Stratified Rule-Aware Network for Abstract Visual Reasoning](https://mlsanity.com/paper/2002.06838/) (2020)
- [Learning Perceptual Inference by Contrasting](https://mlsanity.com/paper/1912.00086/) (2019)
- [Scale-Localized Abstract Reasoning](https://mlsanity.com/paper/2009.09405/) (2020)
- [Abstract Spatial-Temporal Reasoning via Probabilistic Abduction and Execution](https://mlsanity.com/paper/2103.14230/) (2021)
- [Abstract Diagrammatic Reasoning with Multiplex Graph Networks](https://mlsanity.com/paper/2006.11197/) (2020)
- [Few-shot Visual Reasoning with Meta-analogical Contrastive Learning](https://mlsanity.com/paper/2007.12020/) (2020)
- [IQ of Neural Networks](https://mlsanity.com/paper/1710.01692/) (2017)
- [Abstract Visual Reasoning: An Algebraic Approach for Solving Raven's Progressive Matrices](https://mlsanity.com/paper/2303.11730/) (2023)

## Built on by (most cited first, 4 of 4)

- [EarthScape: A Multimodal Dataset for Surficial Geologic Mapping and Earth Surface Analysis](https://mlsanity.com/paper/2503.15625/) (2025)
- [Automated Lane Change Behavior Prediction and Environmental Perception Based on SLAM Technology](https://mlsanity.com/paper/2404.04492/) (2024)
- [The intelligent prediction and assessment of financial information risk in the cloud computing model](https://mlsanity.com/paper/2404.09322/) (2024)
- [A Study of Rule Omission in Raven's Progressive Matrices](https://mlsanity.com/paper/2510.03127/) (2025)

## Abstract (arXiv)

Machine learning models have achieved significant milestones in various domains, for example, computer vision models have an exceptional result in object recognition, and in natural language processing, where Large Language Models (LLM) like GPT can start a conversation with human-like proficiency. However, abstract reasoning remains a challenge for these models, Can AI really thinking like a human? still be a question yet to be answered. Raven Progressive Matrices (RPM) is a metric designed to assess human reasoning capabilities. It presents a series of eight images as a problem set, where the participant should try to discover the underlying rules among these images and select the most appropriate image from eight possible options that best completes the sequence. This task always be used to test human reasoning abilities and IQ. Zhang et al proposed a dataset called RAVEN which can be used to test Machine Learning model abstract reasoning ability. In this paper, we purposed Vision Transformer Contrastive Network which build on previous work with the Contrastive Perceptual Inference network (CoPiNet), which set a new benchmark for permutationinvariant models Raven Progressive Matrices by incorporating contrast effects from psychology, cognition, and education, and extends this foundation by leveraging the cutting-edge Vision Transformer architecture. This integration aims to further refine the machine ability to process and reason about spatial-temporal information from pixel-level inputs and global wise features on RAVEN dataset.