論文筆記：Reference based LSTM for Image Captioning
Reference based LSTM for Image Captioning 這篇文章，在訓練階段和預測階段都加入了references:在訓練階段通過references來判斷訓練captions中出現的每個單詞的重要性；在預測階段則在選擇生成的caption時，使用似然概率和consensus score的加權平均來權衡生成句子的好壞，這樣可以使選出的句子更具準確性。 1.
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相關文章
- 1. Exploring Visual Relationship for Image Captioning論文筆記
- 2. 論文筆記：Areas of Attention for Image Captioning
- 3. 論文筆記：Contrastive Learning for Image Captioning
- 4. Reflective Decoding Network for Image Captioning論文閱讀
- 5. 論文筆記：Self-critical Sequence Training for Image Captioning
- 6. 論文筆記：Image Captioning with Semantic Attention
- 7. 論文筆記：Attention Correctness in Neural Image Captioning
- 8. 論文筆記：X-Linear Attention Networks for Image Captioning
- 9. 論文筆記《Blind Quality Assessment Based on Pseudo-Reference Image》(1)
- 10. 論文筆記《Blind Quality Assessment Based on Pseudo-Reference Image》(2)