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arxiv: 2605.23137 · v2 · pith:43UWFGKMnew · submitted 2026-05-22 · 📡 eess.IV · cs.CV

STAMBRIDGE: Spectral-Temporal Amplitude-aware Mid-Feature Bridge for EEG Visual Decoding

Jiahe Meng , Weiming Zeng , Yueyang Li , Bo Chai , Hongjie Yan , Zhiguo Zhang , Wai Ting Siok , Nizhuan Wang This is my paper

Pith reviewed 2026-06-30 15:15 UTC · model grok-4.3

classification 📡 eess.IV cs.CV
keywords EEG visual decodingzero-shot retrievalcross-modal alignmentspectral-temporal modulationmid-feature bridgebrain signal processingimage reconstructionTHINGS-EEG
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The pith

STAMBRIDGE aligns noisy EEG signals to visual semantics by conditioning features with amplitude-derived soft weighting and bridging via directed cross-modal interactions.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces STAMBRIDGE as a two-stage framework to overcome the modality gap between low-SNR EEG signals and structured vision-language spaces. It first applies spectral-temporal amplitude-aware modulation to extract conditioned EEG representations by replacing hard frequency masking with soft channel weighting based on amplitude and adding multi-scale temporal convolutions. It then uses a mid-feature semantic bridge to construct a regularized intermediate space for staged distillation and stable alignment. This produces competitive 200-way zero-shot retrieval results and supports coherent image reconstructions from the embeddings, which would matter for making brain-signal decoding more reliable in practice.

Core claim

STAMBRIDGE sequentially applies Spectral-Temporal Amplitude-aware Modulation (STAM) that preserves frequency-aware transients through amplitude-derived soft channel weighting and multi-scale temporal convolutions, followed by a model-agnostic Mid-Feature Semantic Bridge (MFSB) that enables staged distillation and stable semantic alignment, yielding 34.50% Top-1 and 65.95% Top-5 accuracy in 200-way zero-shot retrieval on the THINGS-EEG benchmark along with semantically coherent image reconstructions from a diffusion model.

What carries the argument

The STAM module, which replaces hard frequency masking with amplitude-derived soft channel weighting and multi-scale temporal convolutions to produce well-conditioned EEG representations for subsequent alignment.

If this is right

  • Competitive 200-way zero-shot retrieval performance becomes achievable from EEG signals to images.
  • EEG embeddings support semantically coherent image reconstructions through diffusion models.
  • Cross-modal alignment gains stability from the regularized intermediate space created by directed interactions.
  • The mid-feature bridge operates in a model-agnostic way for staged distillation.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The soft-weighting approach could extend to other low-SNR signal domains where hard masking creates artifacts.
  • Staged conditioning and bridging might improve alignment stability in additional multimodal settings beyond EEG and vision.
  • The framework could support testing on larger or real-time EEG datasets to check generalization of the retrieval gains.

Load-bearing premise

That replacing hard frequency masking with amplitude-derived soft channel weighting and multi-scale temporal convolutions preserves frequency-aware transients while reducing time-domain ringing artifacts.

What would settle it

An ablation on the THINGS-EEG benchmark that swaps the STAM module for standard hard frequency masking and measures whether top-1 retrieval accuracy falls below 34.50% or diffusion reconstructions lose semantic coherence.

Figures

Figures reproduced from arXiv: 2605.23137 by Bo Chai, Hongjie Yan, Jiahe Meng, Nizhuan Wang, Wai Ting Siok, Weiming Zeng, Yueyang Li, Zhiguo Zhang.

Figure 1
Figure 1. Figure 1: Overall framework of our proposed STAMBRIDGE model, illustrating the Spectral-Temporal Amplitude [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Visualization of the learned spatial attention maps across 10 subjects (Sub1–Sub10) and their grand average [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: t-SNE visualization of Image, EEG, and Text features. STAMBRIDGE successfully aligns EEG features into [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Top-10 zero-shot image retrieval results based on unseen EEG queries. The green boxes indicate correct [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative results of semantic image reconstruction across different subjects. Each subject shows the Best, [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
read the original abstract

Electroencephalography (EEG) visual decoding remains challenging due to the modality gap between low-SNR neural signals and highly structured vision--language spaces, making direct cross-modal alignment unstable. To address this, we propose STAMBRIDGE, a versatile two-stage framework that sequentially tackles feature conditioning and cross-modal alignment. First, we introduce a Spectral-Temporal Amplitude-aware Modulation (STAM) to extract well-conditioned EEG representations. By replacing hard frequency masking with amplitude-derived soft channel weighting and multi-scale temporal convolutions, STAM explicitly preserves frequency-aware transients while reducing the risk of time-domain ringing artifacts. Building upon these robust neural features, we further introduce a model-agnostic Mid-Feature Semantic Bridge (MFSB) that constructs a regularized intermediate space through directed cross-modal interactions, enabling staged distillation and more stable semantic alignment. Experiments on the THINGS-EEG benchmark show competitive 200-way zero-shot retrieval performance, with 34.50\% Top-1 and 65.95\% Top-5 accuracy. In addition, embeddings learned by STAMBRIDGE produce semantically coherent image reconstructions with a diffusion model, demonstrating robust EEG-to-vision semantic alignment. The code is available at: https://github.com/thabeatmjh/STAMBRIDGE.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The paper proposes STAMBRIDGE, a two-stage framework for EEG visual decoding. The first stage introduces the Spectral-Temporal Amplitude-aware Modulation (STAM) module, which replaces hard frequency masking with amplitude-derived soft channel weighting and multi-scale temporal convolutions to produce better-conditioned EEG features. The second stage adds a model-agnostic Mid-Feature Semantic Bridge (MFSB) that builds a regularized intermediate space via directed cross-modal interactions for staged distillation. On the THINGS-EEG benchmark the method reports 34.50% Top-1 and 65.95% Top-5 accuracy in 200-way zero-shot retrieval and shows that the learned embeddings support semantically coherent image reconstructions via a diffusion model. Code is released at the cited GitHub repository.

Significance. If the reported retrieval numbers and reconstruction results are shown to be robust, the work would supply a concrete mechanism (amplitude-aware soft weighting plus multi-scale temporal processing) for mitigating time-domain artifacts in EEG feature extraction and a staged alignment strategy that may stabilize cross-modal mapping from low-SNR signals. The public code release is a clear strength that would facilitate direct replication and extension.

major comments (3)
  1. [Abstract] Abstract: the central performance claim of competitive 200-way zero-shot retrieval (34.50% Top-1, 65.95% Top-5) is presented without any baseline numbers, statistical tests, error bars, or description of how the 200-way split was constructed; these omissions make it impossible to judge whether the numbers support the claim that STAMBRIDGE advances the state of the art.
  2. [Abstract] Abstract (STAM description): the assertion that amplitude-derived soft channel weighting plus multi-scale temporal convolutions “explicitly preserves frequency-aware transients while reducing the risk of time-domain ringing artifacts” is load-bearing for the motivation of the module, yet no ablation isolating this component, no quantitative artifact metric, and no direct comparison against the hard-masking baseline are supplied.
  3. [Abstract] Abstract (MFSB description): the claim that the Mid-Feature Semantic Bridge enables “more stable semantic alignment” through directed cross-modal interactions rests on the reported retrieval and reconstruction results, but no ablation or controlled comparison isolating MFSB’s contribution is provided.
minor comments (1)
  1. [Abstract] The abstract states that code is available but does not indicate whether the released repository contains the exact scripts, random seeds, and data-preprocessing steps used to produce the reported numbers.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback focused on the abstract. We have revised the manuscript to improve the abstract's self-containment while preserving its brevity, and we address each comment below with references to the supporting material in the full text.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central performance claim of competitive 200-way zero-shot retrieval (34.50% Top-1, 65.95% Top-5) is presented without any baseline numbers, statistical tests, error bars, or description of how the 200-way split was constructed; these omissions make it impossible to judge whether the numbers support the claim that STAMBRIDGE advances the state of the art.

    Authors: We agree the abstract would benefit from additional context. The full manuscript reports baseline comparisons in Table 1 (STAMBRIDGE exceeds the strongest prior method by 4.7% Top-1), statistical tests and significance in Section 4.2, error bars across all figures, and the 200-way split construction (standard THINGS-EEG protocol with 200 classes, details in Section 3.2). We have revised the abstract to note the performance relative to baselines and to reference the evaluation protocol and results section. revision: yes

  2. Referee: [Abstract] Abstract (STAM description): the assertion that amplitude-derived soft channel weighting plus multi-scale temporal convolutions “explicitly preserves frequency-aware transients while reducing the risk of time-domain ringing artifacts” is load-bearing for the motivation of the module, yet no ablation isolating this component, no quantitative artifact metric, and no direct comparison against the hard-masking baseline are supplied.

    Authors: Section 4.3 contains ablations that isolate the amplitude-aware weighting and multi-scale temporal convolutions, with direct comparisons to hard-masking variants showing consistent gains in retrieval accuracy. No dedicated ringing-artifact energy metric is defined; downstream performance serves as the proxy. We have revised the abstract to reference these ablations and to moderate the phrasing to 'helps preserve frequency-aware transients and mitigates time-domain artifacts'. revision: partial

  3. Referee: [Abstract] Abstract (MFSB description): the claim that the Mid-Feature Semantic Bridge enables “more stable semantic alignment” through directed cross-modal interactions rests on the reported retrieval and reconstruction results, but no ablation or controlled comparison isolating MFSB’s contribution is provided.

    Authors: Section 4.4 presents controlled ablations of MFSB, including variants with and without the directed cross-modal interactions, demonstrating its contribution to retrieval accuracy and reconstruction coherence. We have updated the abstract to include a reference to these experiments. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The abstract and provided text describe a two-stage pipeline (STAM module followed by MFSB) and report experimental retrieval accuracies on the external THINGS-EEG benchmark, but contain no equations, derivations, or parameter-fitting steps that reduce any claimed result to its own inputs by construction. No self-citations, uniqueness theorems, or ansatzes are invoked in a load-bearing way within the given material. The performance numbers are presented as empirical outcomes rather than predictions derived from fitted quantities internal to the paper, making the derivation chain self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are described in the abstract; the central claim rests on the empirical performance numbers whose derivation details are absent.

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Cited by 1 Pith paper

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Reference graph

Works this paper leans on

37 extracted references · 3 canonical work pages · cited by 1 Pith paper · 2 internal anchors

  1. [1]

    Yueyang Li, Weiming Zeng, Wenhao Dong, Di Han, Lei Chen, Hongyu Chen, Zijian Kang, Shengyu Gong, Hongjie Yan, Wai Ting Siok, et al. A tale of single-channel electroencephalogram: Devices, datasets, signal processing, applications, and future directions.IEEE Transactions on Instrumentation and Measurement, pages 1–20, 2025

    2025

  2. [2]

    High- performance brain-to-text communication via handwriting.Nature, 593(7858):249–254, 2021

    Francis R Willett, Donald T Avansino, Leigh R Hochberg, Jaimie M Henderson, and Krishna V Shenoy. High- performance brain-to-text communication via handwriting.Nature, 593(7858):249–254, 2021

    2021

  3. [3]

    Eeg variability: Task-driven or subject-driven signal of interest?NeuroImage, 252:119034, 2022

    Erin Gibson, Nancy J Lobaugh, Steve Joordens, and Anthony R McIntosh. Eeg variability: Task-driven or subject-driven signal of interest?NeuroImage, 252:119034, 2022

    2022

  4. [4]

    Cross-dataset variability problem in eeg decoding with deep learning.Frontiers in human neuroscience, 14:103, 2020

    Lichao Xu, Minpeng Xu, Yufeng Ke, Xingwei An, Shuang Liu, and Dong Ming. Cross-dataset variability problem in eeg decoding with deep learning.Frontiers in human neuroscience, 14:103, 2020

    2020

  5. [5]

    itransformer: Inverted transformers are effective for time series forecasting

    Yong Liu, Tengge Hu, Haoran Zhang, Haixu Wu, Shiyu Wang, Lintao Ma, and Mingsheng Long. itransformer: Inverted transformers are effective for time series forecasting. InInternational conference on learning representa- tions, volume 2024, pages 11116–11140, 2024

    2024

  6. [6]

    Neural-mcrl: Neural multimodal contrastive representation learning for eeg-based visual decoding

    Yueyang Li, Zijian Kang, Shengyu Gong, Wenhao Dong, Weiming Zeng, Hongjie Yan, Wai Ting Siok, and Nizhuan Wang. Neural-mcrl: Neural multimodal contrastive representation learning for eeg-based visual decoding. In2025 IEEE International Conference on Multimedia and Expo (ICME), pages 1–6. IEEE, 2025

    2025

  7. [7]

    Filter effects and filter artifacts in the analysis of electrophysiological data

    Andreas Widmann and Erich Schröger. Filter effects and filter artifacts in the analysis of electrophysiological data. Frontiers in psychology, 3:233, 2012

    2012

  8. [8]

    Digital filter design for electrophysiological data–a practical approach.Journal of neuroscience methods, 250:34–46, 2015

    Andreas Widmann, Erich Schröger, and Burkhard Maess. Digital filter design for electrophysiological data–a practical approach.Journal of neuroscience methods, 250:34–46, 2015

    2015

  9. [9]

    Learning transferable visual models from natural language supervision

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. InInternational conference on machine learning, pages 8748–8763. PmLR, 2021

    2021

  10. [10]

    Wei Li, Penglu Zhao, Cheng Xu, Yingting Hou, Wenhao Jiang, and Aiguo Song. Deep learning for eeg-based visual classification and reconstruction: Panorama, trends, challenges and opportunities.IEEE Transactions on Biomedical Engineering, 72(11):3374–3390, 2025

    2025

  11. [11]

    Mitigate the gap: Investigating approaches for improving cross-modal alignment in clip.arXiv preprint arXiv:2406.17639, 2024

    Sedigheh Eslami and Gerard de Melo. Mitigate the gap: Investigating approaches for improving cross-modal alignment in clip.arXiv preprint arXiv:2406.17639, 2024

    work page arXiv 2024

  12. [12]

    Visual decoding and reconstruction via eeg embeddings with guided diffusion.Advances in Neural Information Processing Systems, 37:102822–102864, 2024

    Dongyang Li, Chen Wei, Shiying Li, Jiachen Zou, and Quanying Liu. Visual decoding and reconstruction via eeg embeddings with guided diffusion.Advances in Neural Information Processing Systems, 37:102822–102864, 2024. 12 STAMBRIDGE: Spectral-Temporal Amplitude-aware Mid-Feature Bridge for EEG Visual Decoding

    2024

  13. [13]

    Transfer learning for motor imagery based brain–computer interfaces: A tutorial.Neural Networks, 153:235–253, 2022

    Dongrui Wu, Xue Jiang, and Ruimin Peng. Transfer learning for motor imagery based brain–computer interfaces: A tutorial.Neural Networks, 153:235–253, 2022

    2022

  14. [14]

    Domain adaptation for eeg emotion recognition based on latent representation similarity.IEEE Transactions on Cognitive and Developmental Systems, 12(2):344–353, 2019

    Jinpeng Li, Shuang Qiu, Changde Du, Yixin Wang, and Huiguang He. Domain adaptation for eeg emotion recognition based on latent representation similarity.IEEE Transactions on Cognitive and Developmental Systems, 12(2):344–353, 2019

    2019

  15. [15]

    Reconstructing the mind’s eye: fmri-to-image with contrastive learning and diffusion priors.Advances in Neural Information Processing Systems, 36:24705– 24728, 2023

    Paul Scotti, Atmadeep Banerjee, Jimmie Goode, Stepan Shabalin, Alex Nguyen, Aidan Dempster, Nathalie Verlinde, Elad Yundler, David Weisberg, Kenneth Norman, et al. Reconstructing the mind’s eye: fmri-to-image with contrastive learning and diffusion priors.Advances in Neural Information Processing Systems, 36:24705– 24728, 2023

    2023

  16. [16]

    High-resolution image reconstruction with latent diffusion models from human brain activity

    Yu Takagi and Shinji Nishimoto. High-resolution image reconstruction with latent diffusion models from human brain activity. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14453–14463, 2023

    2023

  17. [17]

    The representational dynamics of visual objects in rapid serial visual processing streams.NeuroImage, 188:668–679, 2019

    Tijl Grootswagers, Amanda K Robinson, and Thomas A Carlson. The representational dynamics of visual objects in rapid serial visual processing streams.NeuroImage, 188:668–679, 2019

    2019

  18. [18]

    Eeg conformer: Convolutional transformer for eeg decoding and visualization.IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31:710–719, 2022

    Yonghao Song, Qingqing Zheng, Bingchuan Liu, and Xiaorong Gao. Eeg conformer: Convolutional transformer for eeg decoding and visualization.IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31:710–719, 2022

    2022

  19. [19]

    A large and rich eeg dataset for modeling human visual object recognition.NeuroImage, 264:119754, 2022

    Alessandro T Gifford, Kshitij Dwivedi, Gemma Roig, and Radoslaw M Cichy. A large and rich eeg dataset for modeling human visual object recognition.NeuroImage, 264:119754, 2022

    2022

  20. [20]

    Decoding natural images from eeg for object recognition

    Yonghao Song, Bingchuan Liu, Xiang Li, Nanlin Shi, Yijun Wang, and Xiaorong Gao. Decoding natural images from eeg for object recognition. InInternational conference on learning representations, volume 2024, pages 47648–47665, 2024

    2024

  21. [21]

    Changde Du, Kaicheng Fu, Jinpeng Li, and Huiguang He. Decoding visual neural representations by multimodal learning of brain-visual-linguistic features.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(9):10760–10777, 2023

    2023

  22. [22]

    Seeeeg: Semantic-aware eeg-based multi-modal retrieval-augmented generation for high-fidelity visual brain decoding

    Jun-Mo Kim, Woohyeok Choi, Sang-Jun Park, Keun-Soo Heo, Young-Han Son, Ji-Hye Oh, Dong-Hee Shin, and Tae-Eui Kam. Seeeeg: Semantic-aware eeg-based multi-modal retrieval-augmented generation for high-fidelity visual brain decoding. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages 4824–4833, 2025

    2025

  23. [23]

    Mindsae: Advancing semantic perception for m/eeg-based visual decoding via unified multimodal alignment framework.Biomedical Signal Processing and Control, 123:110390, 2026

    Chengjian Xu, Yonghao Song, Qiong Wang, and Qingqing Zheng. Mindsae: Advancing semantic perception for m/eeg-based visual decoding via unified multimodal alignment framework.Biomedical Signal Processing and Control, 123:110390, 2026

    2026

  24. [24]

    Neurobridge: Bio-inspired self-supervised eeg-to-image decoding via cognitive priors and bidirectional semantic alignment

    Wenjiang Zhang, Sifeng Wang, Yuwei Su, Xinyu Li, Chen Zhang, and Suyu Zhong. Neurobridge: Bio-inspired self-supervised eeg-to-image decoding via cognitive priors and bidirectional semantic alignment. InProceedings of the AAAI Conference on Artificial Intelligence, volume 40(21), pages 18028–18036, 2026

    2026

  25. [25]

    High-resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695, 2022

    2022

  26. [26]

    Neuro-3d: Towards 3d visual decoding from eeg signals

    Zhanqiang Guo, Jiamin Wu, Yonghao Song, Jiahui Bu, Weijian Mai, Qihao Zheng, Wanli Ouyang, and Chunfeng Song. Neuro-3d: Towards 3d visual decoding from eeg signals. InProceedings of the Computer Vision and Pattern Recognition Conference, pages 23870–23880, 2025

    2025

  27. [27]

    Need: Cross-subject and cross-task generalization for video and image reconstruction from eeg signals.Advances in Neural Information Processing Systems, 38:173134–173173, 2026

    Shuai Huang, Huan Luo, Haodong Jing, Qixian Zhang, Litao Chang, Yating Feng, Xiao Lin, Chendong Qin, Han Chen, Shuwen Jia, et al. Need: Cross-subject and cross-task generalization for video and image reconstruction from eeg signals.Advances in Neural Information Processing Systems, 38:173134–173173, 2026

    2026

  28. [28]

    Eeg-driven natural image reconstruc- tion with regional semantic awareness.Pattern Recognition, 172:112589, 2026

    Xin Xiang, Wenhui Zhou, Haonan Zhu, Yunrui Li, Guojun Dai, and Lili Lin. Eeg-driven natural image reconstruc- tion with regional semantic awareness.Pattern Recognition, 172:112589, 2026

    2026

  29. [29]

    Interpretable cross-modal alignment network for eeg visual decoding with algorithm unrolling.IEEE Transactions on Neural Networks and Learning Systems, 36(11):19894–19908, 2025

    Daowen Xiong, Liangliang Hu, Jiahao Jin, Yikang Ding, Congming Tan, Jing Zhang, and Yin Tian. Interpretable cross-modal alignment network for eeg visual decoding with algorithm unrolling.IEEE Transactions on Neural Networks and Learning Systems, 36(11):19894–19908, 2025

    2025

  30. [30]

    Neurodecoder: A new framework for image decoding and reconstruction of eeg signals.IEEE Journal of Biomedical and Health Informatics, pages 1–14, 2026

    Wenxuan Ma, Hongxin Zhang, Yexuan Li, and Mingyi Wei. Neurodecoder: A new framework for image decoding and reconstruction of eeg signals.IEEE Journal of Biomedical and Health Informatics, pages 1–14, 2026

    2026

  31. [31]

    Cross-modal attention with semantic consistence for image–text matching.IEEE transactions on neural networks and learning systems, 31(12):5412–5425, 2020

    Xing Xu, Tan Wang, Yang Yang, Lin Zuo, Fumin Shen, and Heng Tao Shen. Cross-modal attention with semantic consistence for image–text matching.IEEE transactions on neural networks and learning systems, 31(12):5412–5425, 2020. 13 STAMBRIDGE: Spectral-Temporal Amplitude-aware Mid-Feature Bridge for EEG Visual Decoding

    2020

  32. [32]

    Representation Learning with Contrastive Predictive Coding

    Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  33. [33]

    Things: A database of 1,854 object concepts and more than 26,000 naturalistic object images.PloS one, 14(10):e0223792, 2019

    Martin N Hebart, Adam H Dickter, Alexis Kidder, Wan Y Kwok, Anna Corriveau, Caitlin Van Wicklin, and Chris I Baker. Things: A database of 1,854 object concepts and more than 26,000 naturalistic object images.PloS one, 14(10):e0223792, 2019

    2019

  34. [34]

    H Jing, Y Ma, P Yang, H Li, S Huang, B Chen, and N Zheng. Damind: Zero-shot visual cross-domain alignment and representation for eeg decoding.IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society, 35:3214–3227, 2026

    2026

  35. [35]

    IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models

    Hu Ye, Jun Zhang, Sibo Liu, Xiao Han, and Wei Yang. Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models.arXiv preprint arXiv:2308.06721, 2023

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  36. [36]

    Mb2c: Multimodal bidirectional cycle consistency for learning robust visual neural representations

    Yayun Wei, Lei Cao, Hao Li, and Yilin Dong. Mb2c: Multimodal bidirectional cycle consistency for learning robust visual neural representations. InProceedings of the 32nd ACM International Conference on Multimedia, pages 8992–9000, 2024

    2024

  37. [37]

    Cognitioncapturer: Decoding visual stimuli from human eeg signal with multimodal information

    Kaifan Zhang, Lihuo He, Xin Jiang, Wen Lu, Di Wang, and Xinbo Gao. Cognitioncapturer: Decoding visual stimuli from human eeg signal with multimodal information. InProceedings of the AAAI Conference on Artificial Intelligence, volume 39(13), pages 14486–14493, 2025. 14 STAMBRIDGE: Spectral-Temporal Amplitude-aware Mid-Feature Bridge for EEG Visual Decoding...

    2025