In the last few years, a large number of automatic evaluation metrics have been proposed for evaluating Natural Language Generation (NLG) systems. The rapid development and …
Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications. The …
Y Pan, T Yao, Y Li, T Mei - … of the IEEE/CVF conference on …, 2020 - openaccess.thecvf.com
Recent progress on fine-grained visual recognition and visual question answering has featured Bilinear Pooling, which effectively models the 2nd order interactions across multi …
H Tang, C Yuan, Z Li, J Tang - Pattern Recognition, 2022 - Elsevier
Few-shot fine-grained recognition (FS-FGR) aims to distinguish several highly similar objects from different sub-categories with limited supervision. However, traditional few-shot …
L Huang, W Wang, J Chen… - Proceedings of the IEEE …, 2019 - openaccess.thecvf.com
Attention mechanisms are widely used in current encoder/decoder frameworks of image captioning, where a weighted average on encoded vectors is generated at each time step to …
Nowadays, Deep Neural Networks are among the main tools used in various sciences. Convolutional Neural Network is a special type of DNN consisting of several convolution …
J Yu, M Tan, H Zhang, Y Rui… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
The click feature of an image, defined as the user click frequency vector of the image on a predefined word vocabulary, is known to effectively reduce the semantic gap for fine-grained …
B Chen, W Deng, J Hu - Proceedings of the IEEE/CVF …, 2019 - openaccess.thecvf.com
Attention has become more attractive in person re-identification (ReID) as it is capable of biasing the allocation of available resources towards the most informative parts of an input …
Transfer learning is a cornerstone of computer vision, yet little work has been done to evaluate the relationship between architecture and transfer. An implicit hypothesis in …