Pith. sign in

REVIEW 2 cited by

The Algonauts Project 2023 Challenge: UARK-UAlbany Team Solution

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2308.00262 v1 pith:L22XLF6M submitted 2023-08-01 cs.CV

classification cs.CV
keywords brainchallengeresponsesalgonautsdifferentencoderprojectsolution
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. COBRA: A Continual Learning Approach to Vision-Brain Understanding

    cs.CV 2024-11 conditional novelty 6.0 of 10

    A continual learning architecture with a frozen shared brain encoder and per-subject prompt modules improves fMRI-to-image reconstruction and avoids catastrophic forgetting.

  2. Towards Neural Foundation Models for Vision: Aligning EEG, MEG, and fMRI Representations for Decoding, Encoding, and Modality Conversion

    cs.CV 2024-11 conditional novelty 6.0 of 10

    A contrastive model aligns EEG, MEG, and fMRI activity to CLIP image embeddings, enabling image retrieval from brain signals, neural retrieval from images, and cross-modal neural retrieval.

Pith tools