mlsanityData to 14 Oct 2026

Generic decoding of seen and imagined objects using hierarchical visual features

arXiv 1510.06479 ↗First posted 22 Oct 2015q-bio.NC cs.CVcited by 120

Not on the map. Why?

Abstract (arXiv)

Object recognition is a key function in both human and machine vision. While recent studies have achieved fMRI decoding of seen and imagined contents, the prediction is limited to training examples. We present a decoding approach for arbitrary objects, using the machine vision principle that an object category is represented by a set of features rendered invariant through hierarchical processing. We show that visual features including those from a convolutional neural network can be predicted from fMRI patterns and that greater accuracy is achieved for low/high-level features with lower/higher-level visual areas, respectively. Predicted features are used to identify seen/imagined object categories (extending beyond decoder training) from a set of computed features for numerous object images. Furthermore, the decoding of imagined objects reveals progressive recruitment of higher to lower visual representations. Our results demonstrate a homology between human and machine vision and its utility for brain-based information retrieval.

Key facts. Cited by 120 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Oct 2015.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 6; 2022: 11; 2023: 24; 2024: 29; 2025: 29; 2026: 21.

120 papers.

Sort:

By area:

  1. 2022Seeing Beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for Vision Decodingcited by 144
  2. 2023Getting aligned on representational alignmentcited by 143
  3. 2022Decoding speech perception from non-invasive brain recordingscited by 130
  4. 2023Natural scene reconstruction from fMRI signals using generative latent diffusioncited by 97
  5. 2022Mind Reader: Reconstructing complex images from brain activitiescited by 85
  6. 2023Decoding Natural Images from EEG for Object Recognitioncited by 81
  7. 2023Cinematic Mindscapes: High-quality Video Reconstruction from Brain Activitycited by 71
  8. 2023Brain decoding: toward real-time reconstruction of visual perceptioncited by 66
  9. 2024MindBridge: A Cross-Subject Brain Decoding Frameworkcited by 66
  10. 2022Decoding Visual Neural Representations by Multimodal Learning of Brain-Visual-Linguistic Featurescited by 63
  11. 2022Reconstruction of Perceived Images from fMRI Patterns and Semantic Brain Exploration using Instance-Conditioned GANscited by 55
  12. 2023MindDiffuser: Controlled Image Reconstruction from Human Brain Activity with Semantic and Structural Diffusioncited by 47
38 more
  1. 2023DREAM: Visual Decoding from Reversing Human Visual Systemcited by 44
  2. 2023UniBrain: Unify Image Reconstruction and Captioning All in One Diffusion Model from Human Brain Activitycited by 42
  3. 2023BrainCLIP: Bridging Brain and Visual-Linguistic Representation Via CLIP for Generic Natural Visual Stimulus Decodingcited by 36
  4. 2023Contrast, Attend and Diffuse to Decode High-Resolution Images from Brain Activitiescited by 36
  5. 2023Brain Captioning: Decoding human brain activity into images and textcited by 34
  6. 2024MindTuner: Cross-Subject Visual Decoding with Visual Fingerprint and Semantic Correctioncited by 32
  7. 2023Controllable Mind Visual Diffusion Modelcited by 30
  8. 2021A Systematic Collection of Medical Image Datasets for Deep Learningcited by 29
  9. 2024NeuroClips: Towards High-fidelity and Smooth fMRI-to-Video Reconstructioncited by 26
  10. 2023Improving visual image reconstruction from human brain activity using latent diffusion models via multiple decoded inputscited by 23
  11. 2024Neuro-3D: Towards 3D Visual Decoding from EEG Signalscited by 23
  12. 2024Psychometry: An Omnifit Model for Image Reconstruction from Human Brain Activitycited by 22
  13. 2023Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey)cited by 21
  14. 2023Through their eyes: multi-subject Brain Decoding with simple alignment techniquescited by 21
  15. 2024Visual Neural Decoding via Improved Visual-EEG Semantic Consistencycited by 21
  16. 2023fMRI-PTE: A Large-scale fMRI Pretrained Transformer Encoder for Multi-Subject Brain Activity Decodingcited by 17
  17. 2023Brain-Conditional Multimodal Synthesis: A Survey and Taxonomycited by 15
  18. 2024The Wisdom of a Crowd of Brains: A Universal Brain Encodercited by 15
  19. 2025MindSimulator: Exploring Brain Concept Localization via Synthetic FMRIcited by 15
  20. 2024NeuroBind: Towards Unified Multimodal Representations for Neural Signalscited by 13
  21. 2022Semantic Brain Decoding: from fMRI to conceptually similar image reconstruction of visual stimulicited by 12
  22. 2023MinD-3D: Reconstruct High-quality 3D objects in Human Braincited by 11
  23. 2024Animate Your Thoughts: Decoupled Reconstruction of Dynamic Natural Vision from Slow Brain Activitycited by 11
  24. 2025BrainGuard: Privacy-Preserving Multisubject Image Reconstructions from Brain Activitiescited by 10
  25. 2021Natural Image Reconstruction from fMRI using Deep Learning: A Surveycited by 9
  26. 2023A Survey of Emerging Applications of Diffusion Probabilistic Models in MRIcited by 9
  27. 2025SynBrain: Enhancing Visual-to-fMRI Synthesis via Probabilistic Representation Learningcited by 9
  28. 2025SLIM-Brain: A Data- and Training-Efficient Foundation Model for fMRI Data Analysiscited by 9
  29. 2023Joint fMRI Decoding and Encoding with Latent Embedding Alignmentcited by 8
  30. 2025A Survey on fMRI-based Brain Decoding for Reconstructing Multimodal Stimulicited by 7
  31. 2025ZEBRA: Towards Zero-Shot Cross-Subject Generalization for Universal Brain Visual Decodingcited by 7
  32. 2023MindGPT: Interpreting What You See with Non-invasive Brain Recordingscited by 6
  33. 2023Lite-Mind: Towards Efficient and Robust Brain Representation Networkcited by 6
  34. 2024BRACTIVE: A Brain Activation Approach to Human Visual Brain Learningcited by 6
  35. 2024MinD-3D++: Advancing fMRI-Based 3D Reconstruction with High-Quality Textured Mesh Generation and a Comprehensive Datasetcited by 6
  36. 2025A Survey of fMRI to Image Reconstructioncited by 6
  37. 2025Neurons: Emulating the Human Visual Cortex Improves Fidelity and Interpretability in fMRI-to-Video Reconstructioncited by 6
  38. 2021More Than Meets the Eye: Self-Supervised Depth Reconstruction From Brain Activitycited by 5

50 most cited of 120 All 120 (JSON)