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Generating longer textual sequences when conditioned on the visualinformation is an interesting problem to explore. The challenge hereproliferate over the standard vision conditioned sentence-level generation(e.g., image or video captioning) as it requires to produce a brief andcoherent story describing the visual content. In this paper, we mask thisVision-to-Sequence as Graph-to-Sequence learning problem and approach it withthe Transformer architecture. To be specific, we introduce SparseGraph-to-Sequence Transformer (SGST) for encoding the graph and decoding asequence. The encoder aims to directly encode graph-level semantics, while thedecoder is used to generate longer sequences. Experiments conducted with thebenchmark image paragraph dataset show that our proposed achieve 13.3%improvement on the CIDEr evaluation measure when comparing to the previousstate-of-the-art approach.

Sparse Graph to Sequence Learning for Vision Conditioned Long Textual Sequence Generation