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- PetalView: Fine-grained Location and Orientation Extraction of Street-view Images via Cross-view Local Search6
The PetalView extractors give semantically meaningful features that are equivalent across two drastically different views, and the multi-scale search strategy efficiently inspects the satellite image from coarse to fine granularity to provide sub-meter and sub-degree precision extraction.
- Prototypical Cross-domain Knowledge Transfer for Cervical Dysplasia Visual Inspection5
A novel prototype-based knowledge filtering method to estimate the transferability of cross-domain samples and optimize the shared feature space by aligning the cross- domain image representations simultaneously on domain level with early alignment and class level with supervised contrastive learning, which endows model training and knowledge transfer with stronger robustness.
- Mix-Up Self-Supervised Learning for Contrast-Agnostic Applications4
This work addresses the low variance across images based on cross-domain mix-up and build the pretext task based on two synergistic objectives: image reconstruction and transparency prediction, where an improve-ment of 2.5% ~ 7.4% in top-1 accuracy was obtained compared to existing self-supervised learning methods.
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- Traj2Former: A Local Context-aware Snapshot and Sequential Dual Fusion Transformer for Trajectory Classification2
A novel model termed Traj2Former is proposed to spotlight the spatial distribution of the adjacent trajectory points and enhance the snapshot fusion between the trajectory data and the corresponding spatial contexts, and it can be applied from a trajectory database to a digital map.
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- BL-UDA: Towards Unsupervised Domain-Adaptive Surgical Instrument Segmentation with Source Box Labels–
This work introduces a novel unsupervised domain adaptation framework, BL-UDA, which leverages bounding box annotations for surgical instrument segmentation across domains, and effectively bridges object-level and pixel-level domain adaptation.
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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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