ViTCN: Vision Transformer Contrastive Network For Reasoning
Bo Song Yuanhao Xu Yichao Wu
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.