Learning Visual Semantic Relationships for Efficient Visual Retrieval

By Richang Hong, Yang Yang, Meng Wang, Xiansheng Hua. 2015

103 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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. Xiangnan He
2. Tat-seng Chua
3. Dacheng Tao
4. Liqiang Nie
5. Hanwang Zhang
6. Jing Zhang
7. Xiang Wang
8. Shiv Ram Dubey

Citations in the corpus

• Neural Collaborative Filtering (2017, background): “Multi-media items, such as images and videos, contain much richer visual semantics [16, 41] that can reflect users’ interest.”
• Learn, Imagine and Create: Text-to-Image Generation from Prior Knowledge (2019, methodology): “The proposed textual-visual co-embedding method falls into the category of pairwise multi-modal learning [4, 8, 30, 48].”
• Enhancing Sketch-Based Image Retrieval by CNN Semantic Re-ranking (2020, methodology): “TEXT-BASED image retrieval systems [1]–[4], [60]–[62] retrieve images by some keywords.”
• Adversarial Cross-Modal Retrieval (2017, background): “In this category, several subclasses of approaches can be distinguished: unsupervised [1, 5, 9, 29, 36], pair-wise [5, 11, 25, 43], ranking-based [33, 39] and supervised [7, 30, 31, 40] ones.”