FES-RAG reframes multimodal RAG as fragment-level selection using Fragment Information Gain to outperform document-level methods with up to 27% relative CIDEr gains on M2RAG while shortening context.
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TokenFormer unifies multi-field and sequential recommendation modeling via bottom-full-top-sliding attention and non-linear interaction representations to avoid sequential collapse and deliver state-of-the-art performance.
GaussianHSI uses Voronoi-guided bilateral 2D Gaussian splatting plus a spectral detail enhancement module to perform arbitrary-scale hyperspectral image super-resolution.
SAMIC introduces semantic-aware Mamba blocks and SVD-based redundancy reduction to achieve efficient perceptual image compression with improved rate-distortion-perception tradeoffs.
SatFusion unifies multi-frame super-resolution and pansharpening by extracting semantic features from multiple LR multispectral frames and adding structural details from an HR panchromatic image, with an advanced panchromatic-guided variant.
citing papers explorer
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Purifying Multimodal Retrieval: Fragment-Level Evidence Selection for RAG
FES-RAG reframes multimodal RAG as fragment-level selection using Fragment Information Gain to outperform document-level methods with up to 27% relative CIDEr gains on M2RAG while shortening context.
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TokenFormer: Unify the Multi-Field and Sequential Recommendation Worlds
TokenFormer unifies multi-field and sequential recommendation modeling via bottom-full-top-sliding attention and non-linear interaction representations to avoid sequential collapse and deliver state-of-the-art performance.
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Voronoi-guided Bilateral 2D Gaussian Splatting for Arbitrary-Scale Hyperspectral Image Super-Resolution
GaussianHSI uses Voronoi-guided bilateral 2D Gaussian splatting plus a spectral detail enhancement module to perform arbitrary-scale hyperspectral image super-resolution.
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SAMIC: A Lightweight Semantic-Aware Mamba for Efficient Perceptual Image Compression
SAMIC introduces semantic-aware Mamba blocks and SVD-based redundancy reduction to achieve efficient perceptual image compression with improved rate-distortion-perception tradeoffs.
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SatFusion: A Unified Framework for Enhancing Remote Sensing Images via Multi-Frame and Multi-Source Images Fusion
SatFusion unifies multi-frame super-resolution and pansharpening by extracting semantic features from multiple LR multispectral frames and adding structural details from an HR panchromatic image, with an advanced panchromatic-guided variant.