Joint rotational invariance and adversarial training of a dual-stream Transformer yields state of the art Brain-Score for Area V4

TL;DR

This paper demonstrates that a dual-stream Transformer model, optimized with joint rotational invariance and adversarial training, achieves top performance in predicting brain responses in visual areas, surpassing CNNs in some metrics.

Contribution

It introduces a novel joint optimization approach for Vision Transformers that improves their alignment with human visual brain responses, achieving state-of-the-art Brain-Score results.

Findings

Achieved 2nd place in Brain-Score 2022 competition

Outperformed ResNet50 in explainable variance for V4, IT, and behavior

Joint optimization enhances model robustness and interpretability

Abstract

Modern high-scoring models of vision in the brain score competition do not stem from Vision Transformers. However, in this paper, we provide evidence against the unexpected trend of Vision Transformers (ViT) being not perceptually aligned with human visual representations by showing how a dual-stream Transformer, a CrossViT$~\textit{a la}$ Chen et al. (2021), under a joint rotationally-invariant and adversarial optimization procedure yields 2nd place in the aggregate Brain-Score 2022 competition(Schrimpf et al., 2020b) averaged across all visual categories, and at the time of the competition held 1st place for the highest explainable variance of area V4. In addition, our current Transformer-based model also achieves greater explainable variance for areas V4, IT and Behaviour than a biologically-inspired CNN (ResNet50) that integrates a frontal V1-like computation module (Dapello et al.,2020). To assess the contribution of the optimization scheme with respect to the CrossViT architecture, we perform several additional experiments on differently optimized CrossViT's regarding adversarial robustness, common corruption benchmarks, mid-ventral stimuli interpretation and feature inversion. Against our initial expectations, our family of results provides tentative support for an $\textit{"All roads lead to Rome"}$ argument enforced via a joint optimization rule even for non biologically-motivated models of vision such as Vision Transformers. Code is available at https://github.com/williamberrios/BrainScore-Transformers