Zobrazeno 1 - 10
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pro vyhledávání: '"Yoon, Kanghoon"'
Graph neural networks (GNN) are vulnerable to adversarial attacks, which aim to degrade the performance of GNNs through imperceptible changes on the graph. However, we find that in fact the prevalent meta-gradient-based attacks, which utilizes the gr
Externí odkaz:
http://arxiv.org/abs/2407.19155
The scene graph generation (SGG) task involves detecting objects within an image and predicting predicates that represent the relationships between the objects. However, in SGG benchmark datasets, each subject-object pair is annotated with a single p
Externí odkaz:
http://arxiv.org/abs/2407.15396
Recent studies have revealed that GNNs are vulnerable to adversarial attacks. To defend against such attacks, robust graph structure refinement (GSR) methods aim at minimizing the effect of adversarial edges based on node features, graph structure, o
Externí odkaz:
http://arxiv.org/abs/2402.11837
Scene graph generation (SGG) models have suffered from inherent problems regarding the benchmark datasets such as the long-tailed predicate distribution and missing annotation problems. In this work, we aim to alleviate the long-tailed problem of SGG
Externí odkaz:
http://arxiv.org/abs/2401.09786
Autor:
Kim, Kibum, Yoon, Kanghoon, Jeon, Jaehyeong, In, Yeonjun, Moon, Jinyoung, Kim, Donghyun, Park, Chanyoung
Publikováno v:
CVPR (2024), 28306-28316
Weakly-Supervised Scene Graph Generation (WSSGG) research has recently emerged as an alternative to the fully-supervised approach that heavily relies on costly annotations. In this regard, studies on WSSGG have utilized image captions to obtain unloc
Externí odkaz:
http://arxiv.org/abs/2310.10404
Unsupervised GAD methods assume the lack of anomaly labels, i.e., whether a node is anomalous or not. One common observation we made from previous unsupervised methods is that they not only assume the absence of such anomaly labels, but also the abse
Externí odkaz:
http://arxiv.org/abs/2308.11669
Recent works demonstrate that GNN models are vulnerable to adversarial attacks, which refer to imperceptible perturbation on the graph structure and node features. Among various GNN models, graph contrastive learning (GCL) based methods specifically
Externí odkaz:
http://arxiv.org/abs/2306.13854
Recently, molecular relational learning, whose goal is to predict the interaction behavior between molecular pairs, got a surge of interest in molecular sciences due to its wide range of applications. In this work, we propose CMRL that is robust to t
Externí odkaz:
http://arxiv.org/abs/2305.18451
Recent scene graph generation (SGG) frameworks have focused on learning complex relationships among multiple objects in an image. Thanks to the nature of the message passing neural network (MPNN) that models high-order interactions between objects an
Externí odkaz:
http://arxiv.org/abs/2212.00443
Existing Graph Neural Networks (GNNs) usually assume a balanced situation where both the class distribution and the node degree distribution are balanced. However, in real-world situations, we often encounter cases where a few classes (i.e., head cla
Externí odkaz:
http://arxiv.org/abs/2208.10205