The paper creates a real-world corruption benchmark for promptable video object segmentation and proposes MoGA, which uses object-specific memory to improve robustness and temporal consistency under adverse conditions.
Lora-ir: taming low-rank experts for efficient all-in-one image restoration
3 Pith papers cite this work. Polarity classification is still indexing.
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CEA assembles per-token low-rank residual updates via dense affinities over hyper-adapter-generated components to improve all-in-one image restoration on spatially non-uniform degradations.
TGPNet unifies denoising, cloud removal, shadow removal, deblurring, and SAR despeckling into one model via task-guided prompting and reports state-of-the-art results on a new multi-modal benchmark.
citing papers explorer
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Robust Promptable Video Object Segmentation
The paper creates a real-world corruption benchmark for promptable video object segmentation and proposes MoGA, which uses object-specific memory to improve robustness and temporal consistency under adverse conditions.
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Continuous Expert Assembly: Instance-Conditioned Low-Rank Residuals for All-in-One Image Restoration
CEA assembles per-token low-rank residual updates via dense affinities over hyper-adapter-generated components to improve all-in-one image restoration on spatially non-uniform degradations.
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Task-Guided Prompting for Unified Remote Sensing Image Restoration
TGPNet unifies denoising, cloud removal, shadow removal, deblurring, and SAR despeckling into one model via task-guided prompting and reports state-of-the-art results on a new multi-modal benchmark.