GDLA delivers state-of-the-art accuracy on CT, MRI, ultrasound and dermoscopy segmentation benchmarks while keeping linear O(N) complexity in a PVT encoder-decoder.
Attention is all you need
7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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UNVERDICTED 7representative citing papers
DBAC is a new directional metric for bias amplification in image captions that is less sensitive to sentence encoders and more accurate than LIC, validated on COCO gender and race attributes.
A method learns synthetic-to-real parameter corrections from source languages and transfers them to target languages without any real target data, improving HTR across five languages and six models.
GAPL learns a compact set of canonical forgery prototypes and applies two-stage LoRA training to build a low-variance feature space that improves generalization across GAN and diffusion generators.
GRACE improves ID accuracy by 10.8% and adversarial accuracy by 13.5% on ImageNet-tuned CLIP while holding OOD accuracy near the zero-shot baseline.
PVeRA extends VeRA by making its frozen random low-rank matrices probabilistic, enabling better handling of ambiguities and outperforming prior adapters on the VTAB-1k benchmark.
TimePre unifies MLP speed and MCL distributional power via Stabilized Instance Normalization to deliver SOTA probabilistic accuracy, orders-of-magnitude faster inference, and improved stability over prior MCL methods.
citing papers explorer
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Gated Differential Linear Attention: A Linear-Time Decoder for High-Fidelity Medical Segmentation
GDLA delivers state-of-the-art accuracy on CT, MRI, ultrasound and dermoscopy segmentation benchmarks while keeping linear O(N) complexity in a PVT encoder-decoder.
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A Woman with a Knife or A Knife with a Woman? Measuring Directional Bias Amplification in Image Captions
DBAC is a new directional metric for bias amplification in image captions that is less sensitive to sentence encoders and more accurate than LIC, validated on COCO gender and race attributes.
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Zero-Shot Synthetic-to-Real Handwritten Text Recognition via Task Analogies
A method learns synthetic-to-real parameter corrections from source languages and transfers them to target languages without any real target data, improving HTR across five languages and six models.
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Scaling Up AI-Generated Image Detection with Generator-Aware Prototypes
GAPL learns a compact set of canonical forgery prototypes and applies two-stage LoRA training to build a low-variance feature space that improves generalization across GAN and diffusion generators.
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The Geometry of Robustness: Optimizing Loss Landscape Curvature and Feature Manifold Alignment for Robust Finetuning of Vision-Language Models
GRACE improves ID accuracy by 10.8% and adversarial accuracy by 13.5% on ImageNet-tuned CLIP while holding OOD accuracy near the zero-shot baseline.
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PVeRA: Probabilistic Vector-Based Random Matrix Adaptation
PVeRA extends VeRA by making its frozen random low-rank matrices probabilistic, enabling better handling of ambiguities and outperforming prior adapters on the VTAB-1k benchmark.
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TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting
TimePre unifies MLP speed and MCL distributional power via Stabilized Instance Normalization to deliver SOTA probabilistic accuracy, orders-of-magnitude faster inference, and improved stability over prior MCL methods.