9 citations · 27 across the 16 of their papers we have counts for
16 papers
LOGIN: A Large Language Model Consulted Graph Neural Network Training Framework
Yiran Qiao, Xiang Ao, Yang Liu +3
Recent prevailing works on graph machine learning typically follow a similar methodology that involves designing advanced variants of graph neural networks (GNNs) to maintain the s…
Magic Tokens: Select Diverse Tokens for Multi-modal Object Re-Identification
Pingping Zhang, Yuhao Wang, Yang Liu +2
Single-modal object re-identification (ReID) faces great challenges in maintaining robustness within complex visual scenarios. In contrast, multi-modal object ReID utilizes complem…
How Well Can Transformers Emulate In-context Newton's Method?
Angeliki Giannou, Liu Yang, Tianhao Wang +2
Transformer-based models have demonstrated remarkable in-context learning capabilities, prompting extensive research into its underlying mechanisms. Recent studies have suggested t…
Adversarial Curriculum Graph Contrastive Learning with Pair-wise Augmentation
Xinjian Zhao, Liang Zhang, Yang Liu +2
Graph contrastive learning (GCL) has emerged as a pivotal technique in the domain of graph representation learning. A crucial aspect of effective GCL is the caliber of generated po…
CBVS: A Large-Scale Chinese Image-Text Benchmark for Real-World Short Video Search Scenarios
Xiangshuo Qiao, Xianxin Li, Xiaozhe Qu +5
Vision-Language Models pre-trained on large-scale image-text datasets have shown superior performance in downstream tasks such as image retrieval. Most of the images for pre-traini…
Human-Instruction-Free LLM Self-Alignment with Limited Samples
Hongyi Guo, Yuanshun Yao, Wei Shen +4
Aligning large language models (LLMs) with human values is a vital task for LLM practitioners. Current alignment techniques have several limitations: (1) requiring a large amount o…