4 accepted papers
Text-to-motion (T2M) generation has emerged as a fundamental task. However, existing evaluation metrics often fail to accurately capture the semantic alignment between textual descriptions and generated 3D motions. In this work, we propose VeMo, a novel evaluation framework that leverages the zero-s…
Recent advances in retrieval-based in-context learning (ICL) train the retriever using a classification objective, which categorizes in-context examples (ICEs) into the most useful and the rest based on absolute scores. However, during inference, ICEs are retrieved by score ranking rather than class…
Geo-entity resolution involves linking records that refer to the same entities across different spatial datasets, which underpins location-based services. Given the varying quality of geo-data, this task is known to be challenging, as directly comparing the semantic-centric representations of two en…
Explaining the decisions made by Graph Neural Networks (GNNs) is vital for establishing trust and ensuring fairness in critical applications such as medicine and science. The prevalence of hierarchical structure in real-world graphs/networks raises an important question on GNN interpretability: "On…