Feb 22, 2024 · We propose an efficient graph parsing algorithm to infer the pooling structure, which then drives graph pooling.
Nov 22, 2023 · This paper deals with graph pooling, i.e., compressing graph information into compact representations. The paper contains a mostly experimental ...
Graph Parsing Networks. The official implementation of the paper "Graph Parsing Networks" (ICLR 2024).
Graph pooling compresses graph information into a compact representation. State- of-the-art graph pooling methods follow a hierarchical approach, ...
Graph pooling is built on top of GNNs. It aims to capture graph-level information by compressing a set of nodes and their underlying structure into a more ...
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This work proposes an efficient graph parsing algorithm to infer the pooling structure, which then drives graph pooling, and proposes the resulting Graph ...
Abstract. We investigate the problem of efficiently incorporating high-order features into neural graph-based dependency parsing.
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In this paper, we propose a novel model, i.e., Relation Pars- ing Neural Network (RPNN), to detect human-object in- teractions. Specifically, the network is ...
This paper addresses the task of detecting and recognizing human-object interactions (HOI) in images and videos. We introduce the. Graph Parsing Neural Network ...