Im2Text and Text2Im: Associating Images and Texts for Cross-Modal Retrieval

By C. V. Jawahar. 2014

51 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Retrieval & Search, Multimodal Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Yunchao Wei
2. Shuicheng Yan
3. Yuxin Peng
4. Yi Yang
5. Luc Van Gool
6. Jiashi Feng
7. Dacheng Tao
8. Ming-Hsuan Yang

In Information Retrieval & Search

1. Shuicheng Yan
2. Yuxin Peng
3. Fei Yan
4. Krystian Mikolajczyk
5. Yao Zhao
6. Xin Huang
7. Zhenfeng Zhu
8. Yunzhen Zhao

In Multimodal Learning

1. Yuxin Peng
2. Xin Huang
3. Yunzhen Zhao
4. Shuicheng Yan
5. Fei Yan
6. Krystian Mikolajczyk
7. Yunchao Wei
8. Liang Wang

Citations in the corpus

• Self-Supervised Learning of Visual Features through Embedding Images into Text Topic Spaces (2017, neutral): “[36] do image-to-text and text-to-image retrieval using LDA [3] for data representation.”
• An Overview of Cross-Media Retrieval: Concepts, Methodologies, Benchmarks, and Challenges (2017, methodology): “The compared methods in the experiments include: BITR [20], CCA [18], CCA+SMN [27], CFA [30], CMCP [6], DCMIT [35], HSNN [5], JGRHML [7], JRL [10], LGCFL [85], ml-CCA [26], mv-CCA [25] and S2UPG [11].”
• Deep correlation for matching images and text (2015, methodology): “The structured SVM in [48] constructs a joint space of outer product, and is computationally expensive both in term of time and memory.”
• Cross-Modal Retrieval With CNN Visual Features: A New Baseline (2017, result): “Due to different training/testing division or different problems, we do not compare this paper with other articles such as [46] on this data set.”