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Co-Attention In Visual Question Answering.pdf

On_the_Efficacy_of_Co-Attention_Transformer_Layers.pdf
Preview of Co-Attention in Visual Question Answering
🔗 Source: cbmm.mit.edu
📊 Size: 35.54 MB
📄 Pages: 15 pages
⬇️ Downloads: 143

Summary

- Purpose: Investigates the effectiveness of co-attention transformer layers in guiding visual attention in Visual Question Answering (VQA).
- Key Findings:
- Co-attention transformer modules help networks focus on relevant image regions based on questions.
- Semantic meaning of questions doesn't drive visual attention; specific keywords do.
- Object-based region proposals can restrict the model's focus on task-relevant regions.
- Transformer-based models show higher rank-correlation with human attention maps than previous CNN/LSTM networks.
- Question semantics have little influence on visual attention; only specific keywords drive it.
- Methodology:
- Evaluates the impact of number of object region proposals, question part-of-speech tags, question semantics, number of co-attention layers, and answer accuracy on visual attention.
- Compares neural network attention maps with human attention maps qualitatively and quantitatively.
- Implications: Sheds light on the function and interpretation of co-attention transformer layers, highlights gaps in current networks, and guides future VQA model development.

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Description

We investigate the efficacy of co-attention transformer layers in Visual Question Answering, generating visual attention maps using question-conditioned image attention scores. We evaluate the effect of critical components on visual attention of a state-of-the-art VQA model.

Technical Information

  • File Format: PDF
  • File Size: 35.54 MB
  • Pages: 15
  • Language: EN
  • Total Downloads: 143
  • Last Updated: 2 hours ago

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