1 citations · 2 across the 4 of their papers we have counts for
4 papers
Generative Video Matting
Yongtao Ge, Kangyang Xie, Guangkai Xu +6
Video matting has traditionally been limited by the lack of high-quality ground-truth data. Most existing video matting datasets provide only human-annotated imperfect alpha and fo…
Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning
Chendi Ge, Xin Wang, Zeyang Zhang +5
Continual multimodal instruction tuning is crucial for adapting Multimodal Large Language Models (MLLMs) to evolving tasks. However, most existing methods adopt a fixed architectur…
Correlation-Aware Graph Convolutional Networks for Multi-Label Node Classification
Yuanchen Bei, Weizhi Chen, Hao Chen +5
Multi-label node classification is an important yet under-explored domain in graph mining as many real-world nodes belong to multiple categories rather than just a single one. Alth…
NoisyGL: A Comprehensive Benchmark for Graph Neural Networks under Label Noise
Zhonghao Wang, Danyu Sun, Sheng Zhou +4
Graph Neural Networks (GNNs) exhibit strong potential in node classification task through a message-passing mechanism. However, their performance often hinges on high-quality node…