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Found 8 papers, 8 papers with code
Self-Guided Robust Graph Structure Refinement
However, we have discovered that existing GSR methods are limited by narrowassumptions, such as assuming clean node features, moderate structural attacks, and the availability of external clean graphs, resulting in the restricted applicability in real-world scenarios.
Adaptive Self-training Framework for Fine-grained Scene Graph Generation
To this end, we introduce a Self-Training framework for SGG (ST-SGG) that assigns pseudo-labels for unannotated triplets based on which the SGG models are trained.
LLM4SGG: Large Language Models for Weakly Supervised Scene Graph Generation
Weakly-Supervised Scene Graph Generation (WSSGG) research has recently emerged as an alternative to the fully-supervised approach that heavily relies on costly annotations.
Class Label-aware Graph Anomaly Detection
Unsupervised GAD methods assume the lack of anomaly labels, i. e., whether a node is anomalous or not.
Similarity Preserving Adversarial Graph Contrastive Learning
Recent works demonstrate that GNN models are vulnerable to adversarial attacks, which refer to imperceptible perturbation on the graph structure and node features.
Shift-Robust Molecular Relational Learning with Causal Substructure
To do so, we first assume a causal relationship based on the domain knowledge of molecular sciences and construct a structural causal model (SCM) that reveals the relationship between variables.
Unbiased Heterogeneous Scene Graph Generation with Relation-aware Message Passing Neural Network
Recent scene graph generation (SGG) frameworks have focused on learning complex relationships among multiple objects in an image.
LTE4G: Long-Tail Experts for Graph Neural Networks
After having trained an expert for each balanced subset, we adopt knowledge distillation to obtain two class-wise students, i. e., Head class student and Tail class student, each of which is responsible for classifying nodes in the head classes and tail classes, respectively.
