DARE-EEG is a self-supervised EEG foundation model that enforces mask-invariance via contrastive mask alignment and momentum anchor alignment, plus conv-linear-probing for heterogeneous setups, achieving SOTA accuracy and cross-dataset portability.
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9 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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OVS-DINO structurally aligns DINO with SAM to revitalize attenuated boundary features, achieving SOTA gains of 2.1% average and 6.3% on Cityscapes in weakly-supervised open-vocabulary segmentation.
A latent denoising objective with saliency-aware corruption and contrastive distillation improves visual alignment and corruption robustness in large multimodal models.
PolarMAE is a new unsupervised pre-training method for fetal ultrasound that uses progressive visual-semantic screening, acoustic-bounded constraints, and polar-texture masking to reach state-of-the-art performance on downstream interpretation tasks.
FASA bridges low-level forensic frequency signals and high-level semantic consistency to achieve state-of-the-art localization of both conventional and diffusion-generated image manipulations.
Membership inference attacks can detect whether specific ECG data participated in pretraining self-supervised foundation encoders, with leakage strongest in small cohorts and contrastive models.
DART mitigates structural overfitting in graph missing-feature imputation via global structural augmentation, masked-autoencoder semantic rectification, and test-time distribution rectification, outperforming prior methods on transductive and inductive tasks including a new real-missing dataset.
GenPAS unifies common data augmentation strategies for generative recommendation as special cases of a bias-controlled stochastic sampling process and demonstrates gains in accuracy, data efficiency, and parameter efficiency on benchmarks and industrial data.
citing papers explorer
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DARE-EEG: A Foundation Model for Mining Dual-Aligned Representation of EEG
DARE-EEG is a self-supervised EEG foundation model that enforces mask-invariance via contrastive mask alignment and momentum anchor alignment, plus conv-linear-probing for heterogeneous setups, achieving SOTA accuracy and cross-dataset portability.
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OVS-DINO: Open-Vocabulary Segmentation via Structure-Aligned SAM-DINO with Language Guidance
OVS-DINO structurally aligns DINO with SAM to revitalize attenuated boundary features, achieving SOTA gains of 2.1% average and 6.3% on Cityscapes in weakly-supervised open-vocabulary segmentation.
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Latent Denoising Improves Visual Alignment in Large Multimodal Models
A latent denoising objective with saliency-aware corruption and contrastive distillation improves visual alignment and corruption robustness in large multimodal models.
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PolarMAE: Efficient Fetal Ultrasound Pre-training via Semantic Screening and Polar-Guided Masking
PolarMAE is a new unsupervised pre-training method for fetal ultrasound that uses progressive visual-semantic screening, acoustic-bounded constraints, and polar-texture masking to reach state-of-the-art performance on downstream interpretation tasks.
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Bridging the Micro--Macro Gap: Frequency-Aware Semantic Alignment for Image Manipulation Localization
FASA bridges low-level forensic frequency signals and high-level semantic consistency to achieve state-of-the-art localization of both conventional and diffusion-generated image manipulations.
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Membership Inference Attacks Expose Participation Privacy in ECG Foundation Encoders
Membership inference attacks can detect whether specific ECG data participated in pretraining self-supervised foundation encoders, with leakage strongest in small cohorts and contrastive models.
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Mitigating Structural Overfitting: A Distribution-Aware Rectification Framework for Missing Feature Imputation
DART mitigates structural overfitting in graph missing-feature imputation via global structural augmentation, masked-autoencoder semantic rectification, and test-time distribution rectification, outperforming prior methods on transductive and inductive tasks including a new real-missing dataset.
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Sequential Data Augmentation for Generative Recommendation
GenPAS unifies common data augmentation strategies for generative recommendation as special cases of a bias-controlled stochastic sampling process and demonstrates gains in accuracy, data efficiency, and parameter efficiency on benchmarks and industrial data.
- MAEPose: Self-Supervised Spatiotemporal Learning for Human Pose Estimation on mmWave Video