Fine-Grained Action Retrieval Using Multiple Parts-of-Speech Embeddings скачать в хорошем качестве

Fine-Grained Action Retrieval Using Multiple Parts-of-Speech Embeddings

We address the problem of cross-modal fine-grained action retrieval between text and video. Cross-modal retrieval is commonly achieved through learning a shared embedding space, that can indifferently embed modalities. In this paper, we propose to enrich the embedding by disentangling parts-of-speech (PoS) in the accompanying captions. We build a separate multi-modal embedding space for each PoS tag. The outputs of multiple PoS embeddings are then used as input to an integrated multi-modal space, where we perform action retrieval. This work was accepted at ICCV 2019. More Information can be found at https://mwray.github.io.