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Found 4 papers, 1 papers with code
A Closer Look at Debiased Temporal Sentence Grounding in Videos: Dataset, Metric, and Approach
New benchmarking results indicate that our proposed evaluation protocols can better monitor the research progress.
A Survey on Temporal Sentence Grounding in Videos
In this survey, we give a comprehensive overview for TSGV, which i) summarizes the taxonomy of existing methods, ii) provides a detailed description of the evaluation protocols(i. e., datasets and metrics) to be used in TSGV, and iii) in-depth discusses potential problems of current benchmarking designs and research directions for further investigations.
A Closer Look at Temporal Sentence Grounding in Videos: Dataset and Metric
All the results demonstrate that the re-organized dataset splits and new metric can better monitor the progress in TSGV.
Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks
Embeddings can be learned from both the structural and attribute information of entities, and the results of structure embedding and attribute embedding are combined to get accurate alignments.
Ranked #5 on
Entity Alignment
on YAGO-WIKI50K
