Cross-Subject Mind Decoding from Inaccurate Representations

• URL: http://arxiv.org/abs/2507.19071v1
• Date: Fri, 25 Jul 2025 08:45:02 GMT
• Title: Cross-Subject Mind Decoding from Inaccurate Representations
• Authors: Yangyang Xu, Bangzhen Liu, Wenqi Shao, Yong Du, Shengfeng He, Tingting Zhu,
• Abstract summary: We propose a Bi Autoencoder Intertwining framework for accurate decoded representation prediction.<n>Our method outperforms state-of-the-art approaches on benchmark datasets in both qualitative and quantitative evaluations.
• Score: 42.19569985029642
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Decoding stimulus images from fMRI signals has advanced with pre-trained generative models. However, existing methods struggle with cross-subject mappings due to cognitive variability and subject-specific differences. This challenge arises from sequential errors, where unidirectional mappings generate partially inaccurate representations that, when fed into diffusion models, accumulate errors and degrade reconstruction fidelity. To address this, we propose the Bidirectional Autoencoder Intertwining framework for accurate decoded representation prediction. Our approach unifies multiple subjects through a Subject Bias Modulation Module while leveraging bidirectional mapping to better capture data distributions for precise representation prediction. To further enhance fidelity when decoding representations into stimulus images, we introduce a Semantic Refinement Module to improve semantic representations and a Visual Coherence Module to mitigate the effects of inaccurate visual representations. Integrated with ControlNet and Stable Diffusion, our method outperforms state-of-the-art approaches on benchmark datasets in both qualitative and quantitative evaluations. Moreover, our framework exhibits strong adaptability to new subjects with minimal training samples.

Related papers

• Mind the Gap: Aligning Vision Foundation Models to Image Feature Matching [31.42132290162457]
  We introduce a new framework called IMD (Image feature Matching with a pre-trained Diffusion model) with two parts.<n>Unlike the dominant solutions employing contrastive-learning based foundation models that emphasize global semantics, we integrate the generative-based diffusion models.<n>Our proposed IMD establishes a new state-of-the-art in commonly evaluated benchmarks, and the superior 12% improvement in IMIM indicates our method efficiently mitigates the misalignment.
  arXiv  Detail & Related papers  (2025-07-14T14:28:15Z)
• Solving Inverse Problems with FLAIR [59.02385492199431]
  Flow-based latent generative models are able to generate images with remarkable quality, even enabling text-to-image generation.<n>We present FLAIR, a novel training free variational framework that leverages flow-based generative models as a prior for inverse problems.<n>Results on standard imaging benchmarks demonstrate that FLAIR consistently outperforms existing diffusion- and flow-based methods in terms of reconstruction quality and sample diversity.
  arXiv  Detail & Related papers  (2025-06-03T09:29:47Z)
• Multimodal LLM-Guided Semantic Correction in Text-to-Image Diffusion [52.315729095824906]
  MLLM Semantic-Corrected Ping-Pong-Ahead Diffusion (PPAD) is a novel framework that introduces a Multimodal Large Language Model (MLLM) as a semantic observer during inference.<n>It performs real-time analysis on intermediate generations, identifies latent semantic inconsistencies, and translates feedback into controllable signals that actively guide the remaining denoising steps.<n>Extensive experiments demonstrate PPAD's significant improvements.
  arXiv  Detail & Related papers  (2025-05-26T14:42:35Z)
• PseudoNeg-MAE: Self-Supervised Point Cloud Learning using Conditional Pseudo-Negative Embeddings [55.55445978692678]
  PseudoNeg-MAE enhances global feature representation of point cloud masked autoencoders by making them both discriminative and sensitive to transformations.<n>We propose a novel loss that explicitly penalizes invariant collapse, enabling the network to capture richer transformation cues while preserving discriminative representations.
  arXiv  Detail & Related papers  (2024-09-24T07:57:21Z)
• Diffusion Model Driven Test-Time Image Adaptation for Robust Skin Lesion Classification [24.08402880603475]
  We propose a test-time image adaptation method to enhance the accuracy of the model on test data. We modify the target test images by projecting them back to the source domain using a diffusion model. Our method makes the robustness more robust across various corruptions, architectures, and data regimes.
  arXiv  Detail & Related papers  (2024-05-18T13:28:51Z)
• Forgery-aware Adaptive Transformer for Generalizable Synthetic Image Detection [106.39544368711427]
  We study the problem of generalizable synthetic image detection, aiming to detect forgery images from diverse generative methods. We present a novel forgery-aware adaptive transformer approach, namely FatFormer. Our approach tuned on 4-class ProGAN data attains an average of 98% accuracy to unseen GANs, and surprisingly generalizes to unseen diffusion models with 95% accuracy.
  arXiv  Detail & Related papers  (2023-12-27T17:36:32Z)
• Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction [75.91471250967703]
  We introduce a novel sampling framework called Steerable Conditional Diffusion.<n>This framework adapts the diffusion model, concurrently with image reconstruction, based solely on the information provided by the available measurement.<n>We achieve substantial enhancements in out-of-distribution performance across diverse imaging modalities.
  arXiv  Detail & Related papers  (2023-08-28T08:47:06Z)
• Contrast-augmented Diffusion Model with Fine-grained Sequence Alignment for Markup-to-Image Generation [15.411325887412413]
  This paper proposes a novel model named "Contrast-augmented Diffusion Model with Fine-grained Sequence Alignment" (FSA-CDM) FSA-CDM introduces contrastive positive/negative samples into the diffusion model to boost performance for markup-to-image generation. Experiments are conducted on four benchmark datasets from different domains.
  arXiv  Detail & Related papers  (2023-08-02T13:43:03Z)
• GCD-DDPM: A Generative Change Detection Model Based on Difference-Feature Guided DDPM [7.922421805234563]
  Deep learning methods have recently shown great promise in bitemporal change detection (CD) This work proposes a generative change detection model called GCD-DDPM to directly generate CD maps. Experiments on four high-resolution CD datasets confirm the superior performance of the proposed GCD-DDPM.
  arXiv  Detail & Related papers  (2023-06-06T05:51:50Z)
• Unsupervised Medical Image Translation with Adversarial Diffusion Models [0.2770822269241974]
  Imputation of missing images via source-to-target modality translation can improve diversity in medical imaging protocols. Here, we propose a novel method based on adversarial diffusion modeling, SynDiff, for improved performance in medical image translation.
  arXiv  Detail & Related papers  (2022-07-17T15:53:24Z)
• The Deep Generative Decoder: MAP estimation of representations improves modeling of single-cell RNA data [0.0]
  We present a simple generative model that computes model parameters and representations directly via maximum a posteriori (MAP) estimation. The advantages of this approach are its simplicity and its capability to provide representations of much smaller dimensionality than a comparable VAE.
  arXiv  Detail & Related papers  (2021-10-13T12:17:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.