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Look Closer, Align Better: Explicit Visual Attributes for Robust EEG-to-Image Decoding — arXiv:2611.09123

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.09123
License (as archived): CC0-1.0
Archived: 2026-11-09T22:45:15.912Z
SHA-256 of the archived text: 54d855292726678ac4ca982f238c4d657ed6870443d7efbbbbe91b3ac7a97849
Stable URL of this page: https://metraipolis.com/documento/dc_bee46cab9ead

How to cite

Look Closer, Align Better: Explicit Visual Attributes for Robust EEG-to-Image Decoding — arXiv:2611.09123 — arXiv (en), https://arxiv.org/abs/2611.09123 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bee46cab9ead (Metraipolis archive dc_bee46cab9ead, sha256 54d855292726678ac4ca982f238c4d657ed6870443d7efbbbbe91b3ac7a97849)

BibTeX

@misc{dcbee46cab9ead,
  title = {Look Closer, Align Better: Explicit Visual Attributes for Robust EEG-to-Image Decoding — arXiv:2611.09123},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.09123},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bee46cab9ead; sha256 54d855292726678ac4ca982f238c4d657ed6870443d7efbbbbe91b3ac7a97849},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.09123. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

EEG-to-image retrieval commonly aligns neural responses with representations from pretrained vision encoders. However, these representations may underrepresent fine-grained visual details needed to distinguish semantically similar images. To address this limitation, we first investigate the EEG decodability and complementary predictive value of explicit visual attributes, identifying visual cues that capture EEG-relevant information beyond pretrained representations. Building on these findings, we propose AttrEcho, a framework that enriches pretrained visual representations with complementary, spatially aligned visual attributes. Specifically, Region-Aligned Visual Attribute Fusion (RAVF) integrates EEG-relevant local visual attributes with spatially corresponding pretrained features through a learnable signed residual gate. We further employ Feature Token Aggregation (FTA), which learns dimension-specific weights across regions to aggregate refined visual features for contrastive alignment with EEG representations. Experiments on THINGS-EEG demonstrate that AttrEcho achieves 94.8% Top-1 and 99.7% Top-5 accuracy in intra-subject 200-way retrieval, while maintaining 82.1% Top-1 accuracy as the gallery expands to 1,600 images with same-concept distractors. AttrEcho also achieves 25.3% Top-1 and 53.0% Top-5 accuracy in inter-subject retrieval. These results highlight the importance of EEG-relevant visual attributes and spatially aligned fusion for fine-grained EEG-to-image retrieval.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.CV, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2611.09123 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-09T22:37:47.222Z dalla pagina https://arxiv.org/list/cs.CV/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).