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Robust and Generalizable GNN Fine-Tuning via Uncertainty-aware Adapter Learning
Bo Jiang, Weijun Zhao, Beibei Wang +2
Recently, fine-tuning large-scale pre-trained GNNs has yielded remarkable attention in adapting pre-trained GNN models for downstream graph learning tasks. One representative fine-…
Fine-Grained VLM Fine-tuning via Latent Hierarchical Adapter Learning
Yumiao Zhao, Bo Jiang, Yuhe Ding +3
Adapter-based approaches have garnered attention for fine-tuning pre-trained Vision-Language Models (VLMs) on few-shot classification tasks. These methods strive to develop a light…
Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter
Bo Jiang, Xueyang Ze, Beibei Wang +3
Textual adapter-based tuning methods have shown significant potential in transferring knowledge from pre-trained Vision-Language Models (VLMs) to downstream tasks. Existing works g…
UGG-ReID: Uncertainty-Guided Graph Model for Multi-Modal Object Re-Identification
Xixi Wan, Aihua Zheng, Bo Jiang +3
Multi-modal object Re-IDentification (ReID) has gained considerable attention with the goal of retrieving specific targets across cameras using heterogeneous visual data sources. A…
Reliable Multi-Modal Object Re-Identification via Modality-Aware Graph Reasoning
Xixi Wan, Aihua Zheng, Zi Wang +3
Multi-modal data provides abundant and diverse object information, crucial for effective modal interactions in Re-Identification (ReID) tasks. However, existing approaches often ov…