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深度伪造音频生成与鉴伪技术综述
Last updated on Aug 21, 2025
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深度图表示学习：方法、应用与挑战
Last updated on Aug 21, 2025
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视频深度伪造检测的泛化性问题：方法、挑战与技术进展
Last updated on Aug 21, 2025
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Graph Contrastive Learning with Decoupled Augmentation
Graph contrastive learning based on augmentation strategies has recently demonstrated remarkable performance. Existing methods …
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Graph Contrastive Learning with Decoupled Augmentation
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