This work presents our solutions to the Algonauts Project 2023 Challenge. The primary objective of the challenge revolves around employing computational models to anticipate brain responses captured during participants' observation of intricate natural visual scenes. The goal is to predict brain responses across the entire visual brain, as it is the region where the most reliable responses to images have been observed. We constructed an image-based brain encoder through a two-step training process to tackle this challenge. Initially, we created a pretrained encoder using data from all subjects. Next, we proceeded to fine-tune individual subjects. Each step employed different training strategies, such as different loss functions and objectives, to introduce diversity. Ultimately, our solution constitutes an ensemble of multiple unique encoders. The code is available at https://github.com/uark-cviu/Algonauts2023

翻译：本工作介绍了我们针对阿尔戈号项目2023挑战赛的解决方案。该挑战的核心目标是利用计算模型，预测参与者在观察复杂自然视觉场景时的大脑反应。我们的目标是在整个视觉脑区预测脑反应，因为该区域对图像的反应最为可靠。为应对这一挑战，我们通过两步训练流程构建了基于图像的脑编码器。首先，利用所有受试者的数据训练得到预训练编码器；其次，对每个受试者进行微调。每一步骤均采用不同训练策略（如损失函数和目标函数）以增加多样性。最终，我们的解决方案由多个独特编码器集成的模型构成。代码已公开于 https://github.com/uark-cviu/Algonauts2023