UPADNet applies phase-amplitude decomposition with novel LMMSE estimators inside an unrolled iterative algorithm, outperforming prior deblurring networks on GoPro, RealBlur, and COCO.
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2026 3verdicts
UNVERDICTED 3representative citing papers
MARCO achieves new state-of-the-art semantic correspondence on SPair-71k, AP-10K and PF-PASCAL by combining coarse-to-fine refinement with self-distillation on DINOv2, delivering larger gains at fine thresholds and on unseen keypoints and categories while using 3x fewer parameters and running 10x更快.
CERSA derives low-rank fine-tuning subspaces from SVD principal components that retain 90-95% spectral energy, delivering higher performance than LoRA and other PEFT baselines at substantially lower memory cost across vision, generation, and language tasks.
citing papers explorer
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Leveraging Phase Information to Boost Unrolled Network Learning for Image Deblurring
UPADNet applies phase-amplitude decomposition with novel LMMSE estimators inside an unrolled iterative algorithm, outperforming prior deblurring networks on GoPro, RealBlur, and COCO.
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MARCO: Navigating the Unseen Space of Semantic Correspondence
MARCO achieves new state-of-the-art semantic correspondence on SPair-71k, AP-10K and PF-PASCAL by combining coarse-to-fine refinement with self-distillation on DINOv2, delivering larger gains at fine thresholds and on unseen keypoints and categories while using 3x fewer parameters and running 10x更快.
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CERSA: Cumulative Energy-Retaining Subspace Adaptation for Memory-Efficient Fine-Tuning
CERSA derives low-rank fine-tuning subspaces from SVD principal components that retain 90-95% spectral energy, delivering higher performance than LoRA and other PEFT baselines at substantially lower memory cost across vision, generation, and language tasks.