Fully Convolutional Attention Networks for Fine-Grained Recognition

By Xiao Liu, Tian Xia, Jiang Wang, Yi Yang, Feng Zhou, Yuanqing Lin. 2016

155 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. Yali Wang
2. Peiqin Zhuang
3. Ming Sun
4. Yanming Guo
5. Yu Qiao
6. Yuchen Yuan
7. Errui Ding
8. Jinlin Guo

Citations in the corpus

• This looks like that: deep learning for interpretable image recognition (2018, neutral): “[49]: 77.9 (full); FCAN [26]: 82.0 (full); Neural const.”
• An AI-based Visual Attention Model for Vehicle Make and Model Recognition (2020, unknown): “Fully Convolutional Attention Network (FCAN) was proposed in [17].”
• Learning Rich Part Hierarchies With Progressive Attention Networks for Fine-Grained Image Recognition (2020, unknown): “The attention mechanism is also integrated into fine-grained tasks to obtain discriminative parts, for example, FCANs [32] proposed a reinforcement learning framework to optimally glimpse local discriminative regions, DVAN [33] explicitly pursues the diversity of attention and is able to gather disc”
• Adaptive Triplet Model for Fine-Grained Visual Categorization (2018, background): “However, along with FCAN and MDTP, it cannot be trained without bounding box.”
• Multi-Attention Multi-Class Constraint for Fine-grained Image Recognition (2018, background): “The works in [52] and [26] introduce long short-term memory [12] and reinforcement learning to attention-based classification, respectively.”
• Deep neural network models for computational histopathology: A survey (2019, background): “These kinds of visual attention models have been extensively explored in computer vision applications including object detection (Liu et al., 2016), image classification (Mnih et al., 2014), image captioning (Sharma et al., 2015), and action recognition (Xu et al., 2015b) tasks.”
• WildFish: A Large Benchmark for Fish Recognition in the Wild (2018, background): “To alleviate this difficulty, attention has been proposed recently by two-level domain net [44], reinforcement learning [27], diversified design [49], multi-attention for part learning [50], and recurrent attention [14].”