Dog Breed Classification Using Part Localization

By Jiongxin Liu, Angjoo Kanazawa, David W. Jacobs, Peter N. Belhumeur. 2012

222 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Fine-Grained Visual Classification of Aircraft (2013, background): “Compared to these domains, aircraft classiﬁcation has several interesting aspects.”
Part-Based R-CNNs for Fine-Grained Category Detection (2014, methodology): “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].”
The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition (2015, background): “Fine-grained recognition refers to the task of distinguishing very similar categories, such as breeds of dogs [23, 31], species of birds [54, 52, 5, 3], or models of cars [61, 25].”
The Devil is in the Tails: Fine-grained Classification in the Wild (2017, background): “The vision community has released many ﬁne-grained datasets covering several domains such as birds [5, 50, 52, 56], dogs [23, 34], airplanes [36, 51], ﬂowers [37], leaves [28], trees [55] and cars[24, 32].”
Part Detector Discovery in Deep Convolutional Neural Networks (2014, neutral): “Further qualitative results are presented on the Columbia dogs dataset [28].”
Bird Species Categorization Using Pose Normalized Deep Convolutional Nets (2014, methodology): “Areas explored include feature representations that better preserve fine-grained information [35, 46, 47, 48], segmentation-based approaches [1, 13, 14, 15, 21, 37] that facilitate extraction of purer features, and part/pose normalized feature spaces [5, 6, 19, 33, 38, 39, 43, 50, 51].”
The iNaturalist Challenge 2017 Dataset (2017, background): “The vision community has released many fine-grained datasets covering several domains such as birds [42, 40, 2, 38, 16], dogs [14, 28, 22], airplanes [23, 39], flowers [26], leaves [18], trees [41] and cars [17, 20, 46, 6].”
Deep LAC: Deep localization, alignment and classification for fine-grained recognition (2015, background): “Pioneer work in this area concentrated on constructing discriminative whole-image representation [22, 15, 23].”
Birdsnap: Large-Scale Fine-Grained Visual Categorization of Birds (2014, background): “Much recent work in fine-grained visual categorization has focused on species identification, with work on leaves [14] [25], flowers [19] [25], butterflies [8] [29], insects [17], cats and dogs (e.g., [16] [21]), and birds (e.g., [4] [6] [7] [8] [12] [30] [32] [33]).”
Learning Features and Parts for Fine-Grained Recognition (2014, background): “Some explicitly model pose [2], [18] whereas others use less structured approaches [4], [19]–[21].”
Learning to Navigate for Fine-grained Classification (2018, background): “Some previous works [45,8,3,46,13,2,29] take advantage of fine-grained human annotations, like annotations for bird parts in bird classification.”
Write a Classifier: Predicting Visual Classifiers from Unstructured Text (2015, background): “Diverse applications of fine-grained categories range from classification of natural species [2], [9], [10], [11] to retrieval of different types of M.”
Fine-Grained Crowdsourcing for Fine-Grained Recognition (2013, result): “Our work shares the same end goal as existing work on fine grained recognition [16, 6, 15, 25, 19, 37, 36], but our approach is more aligned with the general line of research that places humans in the loop [20, 11, 22, 29, 9, 5, 24, 23, Figure 2.”
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].”
Local Alignments for Fine-Grained Categorization (2014, background): “semantic parts that are shared by all (or at least most of) the sub-classes, as in (Branson et al., 2011; Liu et al., 2012), such as the head of a dog or the body of a bird, as opposed to distinctive parts, as in (Yao et al.”
Fully Convolutional Attention Localization Networks: Efficient Attention Localization for Fine-Grained Recognition (2016, methodology): “Most conventional methods [7, 8] utilize manually defined parts to localize the regions, such as “the head of a bird”, for fine-grained recognition.”
Augmenting Strong Supervision Using Web Data for Fine-Grained Categorization (2015, methodology): “Various methods have been used to define object parts such as unsupervised patch discovery [32, 8], human-in-the-loop methods [12, 10], or direct reliance on strongly supervised datasets with part-level annotations [21, 4, 33, 20].”
Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection (2015, background): “Various domains have already had datasets introduced including Birds (the CUB-200 [35] and recently announced Birdsnap [2] datasets), Flowers [25, 1], Dogs and Cats [15, 27, 21], Stoneflies [24], Butterflies [19] and Fish [4] along with man-made domains such as Airplanes [23], Cars [16], and Shoes[3”