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- Towards Illumination-Aware Restoration of Metalens-Captured Images: A New Dataset and a Strong Baseline2
IluMeta is introduced---the first and largest real-world, illumination-aware metalens image dataset—captured across diverse lighting environments and a novel end-to-end restoration framework that directs attention to challenging regions and adaptively adjusts to varying illuminations via reinforcement learning is proposed.
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- Integrating SAM Supervision for 3D Weakly Supervised Point Cloud Segmentation1
This work presents a novel approach that maximizes the utility of sparsely available 3D annotations by incorporating segmentation masks generated by 2D foundation models and extends the highly sparse annotations to encompass the areas delineated by 3D masks, thereby substantially augmenting the pool of available labels.
- Next-Generation Metalens Vision System: Powered by AI and Applied to AI1
An end-to-end metalens vision system is presented—from hardware sensing with a custom-built RGB metalens camera, to physics-informed imaging and real-time restoration, and finally to downstream vision applications such as object detection and depth estimation.
- Evidential Robust Feature Learning for Generalized Few-Shot Segmentation–
This work introduces an evidential learning approach that simultaneously improves feature representation robustness and reduces model uncertainty and leverages both general-purpose features from segmentation-centric large pre-trained foundation models and low-level structural features with a physics-informed constraint to improve representations, especially for novel classes.
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- FOCUS: Frequency-Optimized Conditioning of diffUSion models for mitigating catastrophic forgetting during test-time adaptation–
FOCUS is proposed, a novel frequency-based conditioning approach within a diffusion-driven input-adaptation framework that mitigates catastrophic forgetting for recent model adaptation methods and complements existing model adaptation methods.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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