Image segmentation for large-scale subcategory flower recognition

By Anelia Angelova, Shenghuo Zhu, Yuanqing Lin. 2013

63 citations recorded by the source corpus.

Subfields: Molecular Biology, Pharmacology, Plant Science

How researchers see this paper

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

In Molecular Biology

1. Feng Zhou
2. Yuanqing Lin
3. Xiu-Shen Wei
4. Dacheng Tao
5. Chen Zhao
6. Jianchao Yang
7. Trevor Darrell
8. Chunhua Shen

In Plant Science

1. Yuanqing Lin
2. Dacheng Tao
3. Trevor Darrell
4. Chunhua Shen
5. Jianxin Wu
6. Timnit Gebru
7. Feng Zhou
8. Xiu-Shen Wei

Citations in the corpus

• The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition (2015, background): “The majority of research in fine-grained recognition has focused on developing improved models for classification [1,3,5,7,9,8,14,16,18,20,21,22,28,29,36,37,41,42,49,51,50,66,68,69,71,73,72,76,77,75,78].”
• Fine-Grained Recognition in the Wild: A Multi-task Domain Adaptation Approach (2017, background): “While fine-grained image recognition is a well studied problem [2, 5, 8, 10, 11, 9, 16, 17, 19, 26], its real world applicability is hampered by limited available data.”
• Fine-grained recognition without part annotations (2015, background): “birds [44, 47, 4], aircraft [42, 34], cars [41, 27, 32], flowers [35, 1], leaves [30], and dogs [25, 33].”
• Part-Stacked CNN for Fine-Grained Visual Categorization (2015, background): “different species of birds [43, 41, 4], pets [17, 29], flowers [28, 1] and cars California Gull Ring billed Gull The class has its beak mostly different from the class”
• Fine-Grained Categorization and Dataset Bootstrapping Using Deep Metric Learning with Humans in the Loop (2015, background): “For example, in fine-grained flower categorization [33, 34, 3], we want to identify the species of a flower in an image, such as “nelumbo nucifera (lotus flower),” “tulip” or “cherry blossom.””
• Part-Based R-CNNs for Fine-Grained Category Detection (2014, background): “Recently, a large body of computer vision research has focused on the fine-grained classificationproblem in a number of domains, such as animal breeds or species [16, 25, 30, 32, 36, 45], plant species [1, 2, 5, 33, 34, 38], and man-made objects [31, 39].”
• Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization (2019, background): “Fine-Grained Visual Categorization (FGVC) aims to distinguish subordinate objects categories, such as different species of birds [42, 52], and flowers [1].”
• Mask-CNN: Localizing parts and selecting descriptors for fine-grained bird species categorization (2018, background): “Fine-grained recognition tasks such as identifying the species of birds [1, 2], flowers [3, 4] and cars [5], have been popular in applications of computer vision and pattern recognition.”