DiffIML applies score-based generative modeling to image manipulation localization, recovering coherent masks iteratively from noise to improve generalization on unseen manipulation types.
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GTPBD-MM is the first multimodal benchmark for global terraced parcel extraction, integrating image, text, and DEM data with experiments showing that textual and terrain cues improve delineation accuracy over image-only approaches.
GTC improves multi-modal recommendation by using user-conditional diffusion-based feature filtering and total correlation optimization, achieving up to 28.3% gains in NDCG@5 on benchmarks.
citing papers explorer
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Towards Generalized Image Manipulation Localization via Score-based Model
DiffIML applies score-based generative modeling to image manipulation localization, recovering coherent masks iteratively from noise to improve generalization on unseen manipulation types.
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GTPBD-MM: A Global Terraced Parcel and Boundary Dataset with Multi-Modality
GTPBD-MM is the first multimodal benchmark for global terraced parcel extraction, integrating image, text, and DEM data with experiments showing that textual and terrain cues improve delineation accuracy over image-only approaches.
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User-Aware Conditional Generative Total Correlation Learning for Multi-Modal Recommendation
GTC improves multi-modal recommendation by using user-conditional diffusion-based feature filtering and total correlation optimization, achieving up to 28.3% gains in NDCG@5 on benchmarks.