QCMP-CL introduces learnable collaborative sequence augmentation from same-target and similar sequences plus a quality-aware weighting mechanism based on augmentation confidence, outperforming prior CL-based sequential recommendation methods on three real-world datasets.
Kingma and Jimmy Ba
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SGPer combines DINOv2 semantic priors converted to dense prompts with SAM geometric priors through disease-sensitive adapters and dynamic consistency filtering to deliver robust limited-data wheat disease segmentation.
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Quality-Aware Collaborative Multi-Positive Contrastive Learning for Sequential Recommendation
QCMP-CL introduces learnable collaborative sequence augmentation from same-target and similar sequences plus a quality-aware weighting mechanism based on augmentation confidence, outperforming prior CL-based sequential recommendation methods on three real-world datasets.
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Learning to Synergize Semantic and Geometric Priors for Limited-Data Wheat Disease Segmentation
SGPer combines DINOv2 semantic priors converted to dense prompts with SAM geometric priors through disease-sensitive adapters and dynamic consistency filtering to deliver robust limited-data wheat disease segmentation.