arXiv Computer Vision
The Visual Target Matters: Learning across the Visual Hierarchy for Brain-to-Image Retrieval
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
Hugging Face Trending Papers
Sep 21
The Visual Target Matters: Learning across the Visual Hierarchy for Brain-to-Image Retrieval
Brain-to-image retrieval seeks to identify the visual stimulus that elicited a non-invasive neural response. Candidate images are typically represented by pretrained vision models, whose internal repr...
arXiv Computer Vision
Sep 30
Structured Visual Target Learning For Cross-Subject eeg-to-image retrieval
arXiv:2609.36971v1 Announce Type: new Abstract: Cross-subject EEG-to-image retrieval requires a neural represen- tation trained on source subjects to remain aligned with a visual embedding space for...
arXiv AI
Jun 2
Versatile Framework with Semantic and Structural guidance for Image Reconstruction from Brain Activity
arXiv:2606. 00121v1 Announce Type: cross Abstract: Reconstructing visual stimuli from brain recordings has been a meaningful and challenging task in brain decoding.
arXiv Computer Vision
Sep 22
Brain-to-Image Generation: Reconstructing Visual Stimuli from EEG using Generative Adversarial Networks
The paper presents a reproducible single‑subject baseline for reconstructing visual stimuli from EEG using a temporal‑spatial convolutional encoder that maps averaged EEG signals to 512‑dimensional ViT-B/32 image features. On the THINGS‑EEG2 dataset, the model achieves 12.83%, 39.17%, and 58.00% image recall at ranks 1, 5, and 10, respectively, outperforming analytical chance levels. The study also shows that performance drops sharply when applying a model trained on one subject to others, and that direct conditional generators without external visual weights produce noise‑dominated outputs, indicating that only coarse semantic decoding is feasible under the tested protocol.
arXiv Computer Vision
Oct 7
RPA: Residual Patch-Token Adapter for Image Retrieval from EEG and MEG
The paper introduces the Residual Patch Adapter (RPA), a lightweight module that aligns EEG and MEG signals with all patch tokens from an intermediate layer of a Vision Transformer, rather than a single global embedding. Ablation studies show that retaining the full set of patch tokens is crucial for effective alignment, while the CLS token adds little value. Using RPA, the authors achieve state‑of‑the‑art Top‑1 accuracies on THINGS‑EEG2 and THINGS‑MEG datasets, and demonstrate that the approach generalizes to other brain encoders and can be swapped with various neural network backbones.
arXiv Machine Learning
Aug 20
Beyond Trial Averaging: Anchoring Neural and Visual Representations for Few-Repetition Brain-to-Image Retrieval
The paper introduces NEAR, a neural-anchor-based retrieval framework that improves brain-to-image retrieval when only a few neural trials are available. By treating a high‑repetition center as an anchor, NEAR uses a denoiser to pull noisy queries toward the anchor and a small network to predict pseudo anchors for candidate images, thereby aligning both neural and visual representations. Experiments on EEG, MEG, and fMRI datasets show consistent gains, including a 5.7–9.3 percentage point improvement in 200‑way Top‑1 accuracy on THINGS‑EEG2 with only one or four repetitions.