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Homogeneous Graph Extraction: An Approach to Learning Heterogeneous Graph Embedding
Heterogeneous Graph Neural Networks (HGNNs) aim to embed rich structural and semantic information of heterogeneous graphs into …
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Homogeneous Graph Extraction: An Approach to Learning Heterogeneous Graph Embedding
PointActionCLIP: Preventing Transfer Degradation in Point Cloud Action Recognition with a Triple-Path CLIP
Directly applying CLIP to point cloud action recognition can cause severe accuracy collapse. In this paper, we propose PointActionCLIP, …
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PointActionCLIP: Preventing Transfer Degradation in Point Cloud Action Recognition with a Triple-Path CLIP
VisTa: Visual-contextual and Text-augmented Zero-shot Object-level OOD Detection
As object detectors are increasingly deployed as black-box cloud services or pre-trained models with restricted access to the original …
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VisTa: Visual-contextual and Text-augmented Zero-shot Object-level OOD Detection
ACCon: Angle-Compensated Contrastive Regularizer for Deep Regression
In deep regression, capturing the relationship among continuous labels in feature space is a fundamental challenge that has attracted …
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ACCon: Angle-Compensated Contrastive Regularizer for Deep Regression
RUNA: Object-level Out-of-Distribution Detection via Regional Uncertainty Alignment of Multimodal Representations
Enabling object detectors to recognize out-of-distribution (OOD) objects is vital for building reliable systems. A primary obstacle …
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RUNA: Object-level Out-of-Distribution Detection via Regional Uncertainty Alignment of Multimodal Representations
Incremental Label Distribution Learning With Scalable Graph Convolutional Networks
Label Distribution Learning (LDL) is an effective approach for handling label ambiguity, as it can analyze all labels at once and …
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Incremental Label Distribution Learning With Scalable Graph Convolutional Networks
A Novel Optimization Scheme for Named Entity Recognition with Pre-trained Language Models
Named Entity Recognition (NER) is crucial for extracting structured information from text. While traditional methods rely on rules, …
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FormerReckoning: Physics Inspired Transformer for Accurate Inertial Navigation
Although modern localization methods have achieved remarkable accuracy with various sensors, there are still some circumstances where …
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FormerReckoning: Physics Inspired Transformer for Accurate Inertial Navigation
基于分层联邦框架的音频模型生成技术研究
Last updated on Aug 21, 2025
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基于生成对抗网络的多特征融合去雾技术
Last updated on Sep 29, 2025
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