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- Personalized Federated Continual Learning via Multi-Granularity Prompt38
A novel concept called multi-granularity prompt is proposed, i.e., coarse-grained global prompt acquired through the common model learning process, and fine-grained local prompt used to personalize the generalized representation, which achieves the transmission and refinement of common knowledge among clients, further enhancing the performance of personalization.
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- Anti-loss downsampling and dual-granularity context learning for tiny object detection in remote sensing images2
This paper proposes an enhanced architecture that integrates anti-loss downsampling and dual-granularity context learning based on the YOLO framework and introduces a new hybrid downsampling module to replace conventional convolutional downsampling.
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- Improving Adversarial Robustness for 3D Point Cloud Recognition at Test-Time through Purified Self-Training1
The proposed test-time purified self-training strategy is complementary to purification based method in handling continually changing adversarial attacks on the testing data stream and Adaptive thresholding and feature distribution alignment are introduced to improve the robustness of self-training.
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- LLM-Guided Taxonomy and Hierarchical Uncertainty for 3D Point Cloud Active Learning–
This work presents a novel active learning framework for 3D point cloud semantic segmentation that integrates large language models (LLMs) to construct hierarchical label structures and guide uncertainty-based sample selection and establishes hierarchical uncertainty modeling as a powerful paradigm for efficient point cloud annotation.
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- Exploiting Vision Language Model for Training-Free 3D Point Cloud OOD Detection via Graph Score Propagation–
A novel Graph Score Propagation (GSP) method that incorporates prompt clustering and self-training negative prompting to improve OOD scoring with VLM is proposed and demonstrated that GSP consistently outperforms state-of-the-art methods across synthetic and realworld datasets for 3D point cloud OOD detection.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. 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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