# SIMON: Saliency-aware Integrative Multi-view Object-centric Neural Decoding

arXiv: https://arxiv.org/abs/2605.00401 · HTML: https://mlsanity.com/paper/2605.00401/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 28 references that are in the map.
- First posted on arXiv 1 May 2026.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Reproducible scaling laws for contrastive language-image learning](https://mlsanity.com/paper/2212.07143/) (2022)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Bilateral Reference for High-Resolution Dichotomous Image Segmentation](https://mlsanity.com/paper/2401.03407/) (2024)
- [Decoding Natural Images from EEG for Object Recognition](https://mlsanity.com/paper/2308.13234/) (2023)
- [Visual Decoding and Reconstruction via EEG Embeddings with Guided Diffusion](https://mlsanity.com/paper/2403.07721/) (2024)
- [Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features](https://mlsanity.com/paper/1810.10974/) (2018)
- [Bridging the Vision-Brain Gap with an Uncertainty-Aware Blur Prior](https://mlsanity.com/paper/2503.04207/) (2025)
- [Neural system identification for large populations separating "what" and "where"](https://mlsanity.com/paper/1711.02653/) (2017)
- [Visual Neural Decoding via Improved Visual-EEG Semantic Consistency](https://mlsanity.com/paper/2408.06788/) (2024)
- [NeuroBridge: Bio-Inspired Self-Supervised EEG-to-Image Decoding via Cognitive Priors and Bidirectional Semantic Alignment](https://mlsanity.com/paper/2511.06836/) (2025)
- [SUM: Saliency Unification through Mamba for Visual Attention Modeling](https://mlsanity.com/paper/2406.17815/) (2024)
- [HyFI: Hyperbolic Feature Interpolation for Brain-Vision Alignment](https://mlsanity.com/paper/2603.22721/) (2026)

## Abstract (arXiv)

Recent EEG-to-image retrieval methods leverage pretrained vision encoders and foveation-inspired priors, but typically assume a fixed, center-focused view. This center bias conflicts with content-driven human attention, creating a geometric-semantic dissociation between visual features and EEG responses. We propose SIMON, a saliency-aware multi-view framework for zero-shot EEG-to-image retrieval. SIMON combines foreground segmentation and saliency prediction to select fixation centers via Saliency-Aware Sampling (SAS), then generates foveated views that emphasize informative object regions while suppressing background clutter. On THINGS-EEG, SIMON achieves state-of-the-art performance in both intra-subject and inter-subject settings, reaching an average Top-1 accuracy of 69.7% and 19.6%, respectively, consistently outperforming recent competitive baselines. Analyses across sampling granularity, EEG channel topology, and visual/brain encoder backbones further support the robustness of saliency-aware multi-view integration. Our code and models are publicly available at https://github.com/simonlink666/SIMON.