Look, Imagine and Match: Improving Textual-Visual Cross-Modal Retrieval with Generative Models
CVPR2018的一篇关于跨媒体检索的文章，paper链接https://arxiv.org/abs/1711.06420，一作是南洋理工大学的PHD，作者的homepage http://jxgu.cc/，code已经被released出来了https://github.com/ujiuxiang/NLP_Practice.PyTorch/tree/master/cross_modal_retr
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相关文章
- 1. 《Look, Imagine and Match: Improving Textual-Visual Cross-Modal Retrieval with Generative Models》
- 2. IMPROVING GENERATIVE ADVERSARIAL NETWORKS WITH DENOISING FEATURE MATCHING（Bingio-ICLR2017）
- 3. Beyond Part Models- Person Retrieval with Refined Part Pooling
- 4. Beyond Part Models: Person Retrieval with Refined Part Pooling (ECCV2018)
- 5. 【论文精读】Improving Simple Models with Confidence Profiles
- 6. 【ReID】Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional...
- 7. Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline)
- 8. Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline）
- 9. DCGAN应用: Semantic Image Inpainting with Deep Generative Models
- 10. GAUSSIAN MIXTURE VAE: LESSONS IN VARIATIONAL INFERENCE, GENERATIVE MODELS, AND DEEP NETS