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2020.acl-main.683.pdf
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🔗 Source: aclanthology.org
📊 Size: 1.37 MB
👤 Author: Chen Zheng ; Quan Guo ; Parisa Kordjamshidi
⬇️ Downloads: 50

Summary

Researchers propose a cross-modality relevance (CMR) framework to learn representations for language and vision tasks, such as visual question answering (VQA) and natural language for visual reasoning (NLVR). The CMR framework considers entity relevance and high-order relational relevance between the two modalities, with an alignment of representation spaces, and can be trained end-to-end with customizable target tasks. Experiments on VQA and NLVR tasks using public benchmarks show competitive performance and improvement over state-of-the-art published results. The learned alignments and relevance representations from the NLVR task also boost the training efficiency of the VQA task.

Description

Researchers propose a cross-modality relevance (CMR) framework to learn representations for language and vision tasks, such as visual question answering (VQA)...

Technical Information

  • File Format: PDF
  • File Size: 1.37 MB
  • Pages: 10
  • Language: EN
  • Author: Chen Zheng ; Quan Guo ; Parisa Kordjamshidi
  • Total Downloads: 50
  • Last Updated: 6 hours ago

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