mlsanityData to 7 Oct 2026

The Algonauts Project 2023 Challenge: UARK-UAlbany Team Solution

arXiv 2308.00262 ↗First posted 1 Aug 2023cs.CVcited by 11

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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

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  3. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  4. 2017Squeeze-and-Excitation Networkscited by 4,763
  5. 2022A ConvNet for the 2020scited by 3,545
  6. 2023The Algonauts Project 2023 Challenge: How the Human Brain Makes Sense of Natural Scenescited by 34
  7. 2021The Algonauts Project 2021 Challenge: How the Human Brain Makes Sense of a World in Motioncited by 17

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 7; 2025: 2; 2026: 1.

11 papers.