9 accepted papers
Vision-based 3D Semantic Scene Completion (SSC) has received growing attention due to its potential in autonomous driving. While most existing approaches follow an ego-centric paradigm by aggregating and diffusing features over the entire scene, they often overlook fine-grained object-level details,
Quaternion contains one real part and three imaginary parts, which provided a more expressive hypercomplex space for learning knowledge graph. Existing quaternion embedding models measure the plausibility of a triplet either through semantic matching or distance scoring functions. However, it appear…
Document-level Event Extraction (DEE) aims to identify event information from a given document. The two challenges of this task are the event arguments scattering across differrent sentences and the multiple events within a single document. In this paper, we propose a novel Dynamic Structure Hypergr…
Multimodal knowledge graph embedding refers to learning multimodal entities and their relation representations in a low-dimensional space. However, existing multimodal embedding models tend to ignore the inherent structure of knowledge graphs. To address this issue, we propose a novel multimodal kno…
Multi-modal Entity Alignment (MMEA) aims to identify the same entities exhibited in different knowledge graphs (KGs), where the entities are enriched by structure and visual information. Existing MMEA methods learn multi-modal joint entity embeddings by encompassing both modality interaction and mod…
Biomedical Named Entity Recognition (BioNER) aims to identify and classify entities in biomedical text. This task struggles with data scarcity due to limited annotated data. Although data augmentation is effective, existing methods fail to handle the complex semantic mappings on biomedical terminolo…
Entity alignment (EA) aims to match identical entities across different knowledge graphs (KGs). Graph neural network-based entity alignment methods have achieved promising results in Euclidean space. However, KGs often contain complex local and hierarchical structures, which are hard to represent in…
Linear Graph Convolutional Networks (GCNs) are used to classify the node in the graph data. However, we note that most existing linear GCN models perform neural network operations in Euclidean space, which do not explicitly capture the tree-like hierarchical structure exhibited in real-world dataset…
It has always been expected that the robot can understand the natural language instruction and thus a more natural human-robot interaction is achieved. Currently, the robot usually interprets the instruction by visually grounding the textual information to its surroundings, while it may be not enoug…