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[2503.06437] SEED: Towards More Accurate Semantic Evaluation for Visual Brain Decoding
Summary
The SEED metric enhances semantic evaluation in visual brain decoding by integrating multiple metrics, revealing limitations in existing models and offering open-source evaluation data.
Why It Matters
This research addresses the critical need for improved evaluation methods in visual brain decoding, a field that merges neuroscience and machine learning. By introducing SEED, the authors provide a more accurate tool for assessing model performance, which can lead to advancements in understanding brain activity and improving AI models. The open-source nature of the human evaluation data encourages further research and innovation in this area.
Key Takeaways
- SEED integrates three metrics for better semantic evaluation.
- It outperforms existing metrics in aligning with human evaluations.
- The study reveals significant information loss in current decoding models.
- Open-source human evaluation data is provided to foster further research.
- The findings highlight the limitations of traditional evaluation practices.
Computer Science > Computer Vision and Pattern Recognition arXiv:2503.06437 (cs) [Submitted on 9 Mar 2025 (v1), last revised 24 Feb 2026 (this version, v2)] Title:SEED: Towards More Accurate Semantic Evaluation for Visual Brain Decoding Authors:Juhyeon Park, Peter Yongho Kim, Jiook Cha, Shinjae Yoo, Taesup Moon View a PDF of the paper titled SEED: Towards More Accurate Semantic Evaluation for Visual Brain Decoding, by Juhyeon Park and 4 other authors View PDF HTML (experimental) Abstract:We present SEED (Semantic Evaluation for Visual Brain Decoding), a novel metric for evaluating the semantic decoding performance of visual brain decoding models. It integrates three complementary metrics, each capturing a different aspect of semantic similarity between images inspired by neuroscientific findings. Using carefully crowd-sourced human evaluation data, we demonstrate that SEED achieves the highest alignment with human evaluation, outperforming other widely used metrics. Through the evaluation of existing visual brain decoding models with SEED, we further reveal that crucial information is often lost in translation, even in the state-of-the-art models that achieve near-perfect scores on existing metrics. This finding highlights the limitations of current evaluation practices and provides guidance for future improvements in decoding models. Finally, to facilitate further research, we open-source the human evaluation data, encouraging the development of more advanced evaluation m...
