EventRR builds a Referential Event Graph from AMR parsing of the referring expression and uses graph-guided temporal reasoning over detector queries to select and segment the referent, reporting state-of-the-art results on four RVOS benchmarks.
A Graph-to-Sequence Model for AMR-to-Text Generation
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abstract
The problem of AMR-to-text generation is to recover a text representing the same meaning as an input AMR graph. The current state-of-the-art method uses a sequence-to-sequence model, leveraging LSTM for encoding a linearized AMR structure. Although being able to model non-local semantic information, a sequence LSTM can lose information from the AMR graph structure, and thus faces challenges with large graphs, which result in long sequences. We introduce a neural graph-to-sequence model, using a novel LSTM structure for directly encoding graph-level semantics. On a standard benchmark, our model shows superior results to existing methods in the literature.
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2025 1verdicts
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EventRR: Event Referential Reasoning for Referring Video Object Segmentation
EventRR builds a Referential Event Graph from AMR parsing of the referring expression and uses graph-guided temporal reasoning over detector queries to select and segment the referent, reporting state-of-the-art results on four RVOS benchmarks.