Bilinear CNNs for Fine-grained Visual Recognition

By Tsung-Yu Lin, Aruni RoyChowdhury, Subhransu Maji. 2015

97 citations recorded by the source corpus.

How researchers see this paper

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

1. Xiaogang Wang
2. Wanli Ouyang
3. Yi Yang
4. Wei David Liu
5. Shuang Li
6. Liang-Chieh Chen
7. Liang Zheng
8. Dacheng Tao

Citations in the corpus

• DSSD : Deconvolutional Single Shot Detector (2017, methodology): “We also try to use the approximate bilinear pooling method[9], the low-dimensional approximation of the original method proposed by Lin et al. [17], but training speed is slowing down and the training error decrease very slowly as well.”
• Seeing through Sounds: Predicting Visual Semantic Segmentation Results from Multichannel Audio Signals (2019, methodology): “To avoid this problem, we propose the adoption of bilinear feature fusion [12, 13, 14], which has proved effective for modeling the inherent interactions behind spatially unaligned features with fewer parameters.”
• Diversity Regularized Spatiotemporal Attention for Video-Based Person Re-identification (2018, background): “Similar to fine-grained object recognition [26], we pool information across frames to created an enhanced variant”