Birdlets: Subordinate categorization using volumetric primitives and pose-normalized appearance

By Ryan Farrell, Om Oza, Ning Zhang, Vlad I. Morariu, Trevor Darrell, Larry S. Davis. 2011

224 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Social Psychology

How researchers see this paper

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

In Aerospace Engineering

1. Yuanqing Lin
2. Jitendra Malik
3. Feng Zhou
4. Trevor Darrell
5. Ross B. Girshick
6. Subhransu Maji
7. Li Fei-Fei
8. Bernt Schiele

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Dacheng Tao
3. Trevor Darrell
4. Xiaogang Wang
5. Ming-Hsuan Yang
6. Jitendra Malik
7. Bernt Schiele
8. Andrew Zisserman

In Social Psychology

1. Li Fei-Fei
2. Jia Deng
3. Yuanqing Lin
4. Jonathan Krause
5. Peter N. Belhumeur
6. Trevor Darrell
7. Ning Zhang
8. Bangpeng Yao

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 [2, 5, 7, 15, 17, 18, 24, 30, 37, 44, 45, 58, 59, 60, 63, 64].”
• Bilinear CNN Models for Fine-Grained Visual Recognition (2015, background): “Recently variants of such models based on convolutional neural networks (CNNs) [2, 38] have been shown to significantly improve over earlier work that relied on hand-crafted features [1, 11, 39].”
• PANDA: Pose Aligned Networks for Deep Attribute Modeling (2013, background): “Several approaches [11, 26] have used poselets as a part localization scheme for fine-grained categorization tasks which are related to attribute prediction.”
• The Devil is in the Tails: Fine-grained Classification in the Wild (2017, background): “Most fine-grained research papers present a novel model for classification [4, 6, 7, 12, 15, 17, 25, 31, 40, 45, 46, 57, 58, 59, 61, 62].”
• Large Scale Visual Recognition (2012, background): “Our work is also inspired by an emerging trend in computer vision studying large scale visual recognition [98, 36, 83, 30, 137, 52, 125, 20, 138], as well as fine-grained categorization [104, 139, 140, 141, 142].”
• Leveraging the Wisdom of the Crowd for Fine-Grained Recognition (2016, background): “Index Terms—Object recognition, Crowdsourcing, human computation, Gamification Ç”
• How Do You Tell a Blackbird from a Crow? (2013, background): “, trees [13], flowers [16], butterflies [8, 27], dogs [15, 18, 19], and birds [1, 6, 8, 9, 29, 30].”
• Unsupervised Template Learning for Fine-Grained Object Recognition (2012, background): “An increasing number of papers have focused on fine-grained object recognition in recent years [5, 6, 1, 7, 8, 9].”
• Fine-Grained Crowdsourcing for Fine-Grained Recognition (2013, methodology): “Existing approaches either ask humans to label pre-defined parts and attributes [3, 13], or assign open-ended tasks such as proposing semantic labels [20, 11, 22, 9] and specifying visual”
• Fine-Grained Image Classification by Exploring Bipartite-Graph Labels (2015, background): “Various pose-normalization pooling strategies combined with 2D or 3D geometry have been proposed for recognizing birds [7, 17, 20, 62, 63, 64], dogs [37] and cars [28, 30].”
• 3D Object Representations for Fine-Grained Categorization (2013, background): “Third, instead of focusing on a single representation for spatial pooling (two 3D ellipsoids for a bird's head and body for [9]), we compare and extend into 3D two different , state-of-the-art 2D methods for spatial pooling (SPM and BB), demonstrating improved performance.”
• Which Looks Like Which: Exploring Inter-class Relationships in Fine-Grained Visual Categorization (2014, methodology): “We compare with the following published approaches: multiple kernel learning (MKL) [11], Birdlet [19], a random template method [41], and the KDES [10].”
• Multi-Attention Multi-Class Constraint for Fine-grained Image Recognition (2018, methodology): “A straightforward way is supervised learning with manual object part annotations, which has shown promising results in classifying birds [9,3,10,11], dogs [18,5,6,10], and cars [19,4,20].”
• Fine-Grained Visual Comparisons with Local Learning (2014, unknown): “, bird species [8, 14].”
• Learning a Discriminative Filter Bank Within a CNN for Fine-Grained Recognition (2016, background): “ response map. vantages of both categories of previous approaches. 2. Related Work Fine-grained recognition Research in fine-grained recognition has shifted to deep learning from multistage frameworks [48,38,13,30,34,29,8,53,4,3,49,6, 35,10,54,14] based on hand-crafted features such as utilizing DPM ”
• Multiple Granularity Descriptors for Fine-Grained Categorization (2015, methodology): “Several approaches used poselets[4], a part detector based on key-points to capture a specific viewpoint and pose, as a part localization method for fine-grained classification[12][37].”
• Weakly Supervised Fine-Grained Image Categorization (2015, methodology): “For example, in [8, 33], the poselet [2] is used to detect object parts.”
• Deep LAC: Deep localization, alignment and classification for fine-grained recognition (2015, background): “Farrell et al. [7] and Yao et al. [26] used templates to get the location of parts.”