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- OneRestore: A Universal Restoration Framework for Composite Degradation148
This study proposes OneRestore, a novel transformer-based framework designed for adaptive, controllable scene restoration that consolidates four physical corruption paradigms to accurately represent complex, composite degradation scenarios.
- Connecting Giants: Synergistic Knowledge Transfer of Large Multimodal Models for Few-Shot Learning60
Experimental results on four FSL datasets demonstrate that SynTrans, even when paired with a simple few-shot vision encoder, significantly outperforms current state-of-the-art methods.
- D3still: Decoupled Differential Distillation for Asymmetric Image Retrieval18
The Decoupled Differential Distillation (D3still) framework shifts from absolute one-to-one supervision to optimizing the relational differences in pairwise similarities produced by the query and gallery networks, thereby preserving a consistent retrieval order across both networks.
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- SketchCrafter: Sketch Extraction as a Training‐Free Generative Process–
Experiments show that SketchCrafter excels in content alignment and style fidelity across benchmarks, proving it as an effective solution for training‐free sketch extraction.
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- Regional crowd flow estimation from aerial view–
This paper presents a crowd flow estimation model whose goal is to estimate the flow into and out-of a certain region over any given time span and shows that it surpasses the performance of prior methods and can also be applied in a variety of crowd analysis applications for understanding social scenes.
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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