VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
1、前言 1.論文要解決的問題： 輸入： MSCOCO，Flickr30K 輸出： image to text （text to image）:rankhtml 2, 本文的方法創新： 提出了一種新的計算loss的方案，主要針對與hard negtive,加大樣本與hard negtive 的距離web 2、論文方法 Embedding 1) 圖像採用VGG19或者ResNet152進行特徵提取
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相關文章
- 1. Improving Chemical Named Entity Recognition in Patents with Contextualized Word Embeddings
- 2. Improved Embeddings with Easy Positive Triplet Mining
- 3. Look, Imagine and Match: Improving Textual-Visual Cross-Modal Retrieval with Generative Models
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- 5. True(False) Positives (Negatives)
- 6. Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings
- 7. 論文解讀：Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings
- 8. Bootstrapping Face Detection with Hard Negative Examples
- 9. Tune Your Hard Disk with hdparm
- 10. ACL2020《Improving Entity Linking through Semantic Reinforced Entity Embeddings》閱讀筆記