The Algonauts Project 2023 Challenge: UARK-UAlbany Team Solution
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Abstract (arXiv)
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
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 7 of its 9 references that are in the map. First posted on arXiv 1 Aug 2023.
Builds on
7 of 9 references.
References · 7
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
- 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
- 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
- 2017Squeeze-and-Excitation Networkscited by 4,763
- 2022A ConvNet for the 2020scited by 3,545
- 2023The Algonauts Project 2023 Challenge: How the Human Brain Makes Sense of Natural Scenescited by 34
- 2021The Algonauts Project 2021 Challenge: How the Human Brain Makes Sense of a World in Motioncited by 17
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Citing papers per year: 2023: 1; 2024: 7; 2025: 2; 2026: 1.
11 papers.
- 2023Brain Decodes Deep Netscited by 14
- 2026A foundation model of vision, audition, and language for in-silico neurosciencecited by 12
- 2024QClusformer: A Quantum Transformer-based Framework for Unsupervised Visual Clusteringcited by 11
- 2024Quantum Visual Feature Encoding Revisitedcited by 10
- 2024Towards Neural Foundation Models for Vision: Aligning EEG, MEG, and fMRI Representations for Decoding, Encoding, and Modality Conversioncited by 9
- 2024Hierarchical Quantum Control Gates for Functional MRI Understandingcited by 8
- 2024BRACTIVE: A Brain Activation Approach to Human Visual Brain Learningcited by 6
- 2025Multimodal Recurrent Ensembles for Predicting Brain Responses to Naturalistic Movies (Algonauts 2025)cited by 5
- 2024COBRA: A Continual Learning Approach to Vision-Brain Understandingcited by 4
- 2024Generalizability analysis of deep learning predictions of human brain responses to augmented and semantically novel visual stimulicited by 0
- 2025BRAIN: Bias-Mitigation Continual Learning Approach to Vision-Brain Understandingcited by 0
- 2026A foundation model of vision, audition, and language for in-silico neuroscience5 May 2026
- 2025BRAIN: Bias-Mitigation Continual Learning Approach to Vision-Brain Understanding25 Aug 2025
- 2025Multimodal Recurrent Ensembles for Predicting Brain Responses to Naturalistic Movies (Algonauts 2025)23 Jul 2025
- 2024COBRA: A Continual Learning Approach to Vision-Brain Understanding25 Nov 2024
- 2024Towards Neural Foundation Models for Vision: Aligning EEG, MEG, and fMRI Representations for Decoding, Encoding, and Modality Conversion14 Nov 2024
- 2024Generalizability analysis of deep learning predictions of human brain responses to augmented and semantically novel visual stimuli6 Oct 2024
- 2024Hierarchical Quantum Control Gates for Functional MRI Understanding7 Aug 2024
- 2024Quantum Visual Feature Encoding Revisited30 May 2024
- 2024QClusformer: A Quantum Transformer-based Framework for Unsupervised Visual Clustering30 May 2024
- 2024BRACTIVE: A Brain Activation Approach to Human Visual Brain Learning29 May 2024
- 2023Brain Decodes Deep Nets3 Dec 2023