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Data-free Black-box Knowledge Amalgamation
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Data-free Black-box Knowledge Amalgamation
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
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
RREH: Reconstruction Relations Embedded Hashing for Semi-Paired Cross-Modal Retrieval
Known for efficient computation and easy storage, hashing has been extensively explored in cross-modal retrieval. The majority of …
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RREH: Reconstruction Relations Embedded Hashing for Semi-Paired Cross-Modal Retrieval