Dual-Path Convolutional Image-Text Embedding
By Zhedong Zheng, Liang Zheng, Michael Garrett, Yi Yang, Yi-Dong Shen. 2017
77 citations recorded by the source corpus.
Subfields: Multimodal Learning
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In Multimodal Learning
Citations in the corpus
- Webly Supervised Joint Embedding for Cross-Modal Image-Text Retrieval (2018, methodology): “Recently, focusing on hard-negatives has been shown to be effective in learning joint embeddings [8, 44, 56, 60].”
- Stacked Cross Attention for Image-Text Matching (2018, background): “The best R@1 of sentence retrieval given an image query is 67.9, achieved by SCAN i-t AVG, where we see a 22.1% relative improvement comparing to DPC [45].”
- Knowledge Aware Semantic Concept Expansion for Image-Text Matching (2019, background): “[Zheng et al., 2017] proposed using a dual task to embed image and text to a shared visual-textual space discriminatively.”
- Similarity Reasoning and Filtration for Image-Text Matching (2021, methodology): “Similarity Prediction Most existing works (Faghri et al. 2017; Wang, Li, and Lazebnik 2016; Zheng et al. 2017; Vendrov et al. 2016; Gu et al. 2018) for image-text matching learned the joint embedding and the similarity measures for cross-modal matching.”
- CAMP: Cross-Modal Adaptive Message Passing for Text-Image Retrieval (2019, background): “[34] explored text CNN and instance loss to learn more discriminative embeddings of images and sentences.”
- IMRAM: Iterative Matching With Recurrent Attention Memory for Cross-Modal Image-Text Retrieval (2020, unknown): “Image Retrieval R@sum R@1 R@5 R@10 R@1 R@5 R@10 1K DPC [28] 65.6 89.8 95.5 47.1 79.9 90.0 467.9 SCO [7] 69.9 92.9 97.”