BiCoS: A Bi-level co-segmentation method for image classification

By Yuning Chai, Victor S. Lempitsky, Andrew Zisserman. 2011

204 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

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].”
What's the Point: Semantic Segmentation with Point Supervision (2015, methodology): “[14,35,36] use co-segmentation methods trained from image-level labels to automatically infer the segmentations.”
Bird Species Categorization Using Pose Normalized Deep Convolutional Nets (2014, background): “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].”
Fully Convolutional Attention Networks for Fine-Grained Recognition (2016, background): “[38] segment images and align the image segments in an unsupervised fashion.”
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.”
Local Alignments for Fine-Grained Categorization (2014, methodology): “This allows us to draw conclusions independent of the segmentation model unlike (Nilsback and Zisserman 2008; Chai et al. 2011, 2012).”
Image segmentation for large-scale subcategory flower recognition (2013, methodology): “Similarly, in the co-segmentation body of works [2, 9], better models are trained by exploiting shared appearance features in images containing the same class of objects.”
Diversified Visual Attention Networks for Fine-Grained Object Classification (2016, background): “In alignment categorization [37], [38], images are first segmented and then objects are roughly aligned, from which features at different aligned parts are extracted for classification.”
Nonparametric Part Transfer for Fine-Grained Recognition (2014, unknown): “Reducing background clutter which might interfere classification is presented in [6, 25] using segmentation techniques.”
A survey on deep learning-based fine-grained object classification and semantic segmentation (2017, background): “Similarly, Gavves et al. [17] and Chai et al. [18] segment images and align the image segments in an unsupervised fashion.”
The Ignorant Led by the Blind: A Hybrid Human–Machine Vision System for Fine-Grained Categorization (2014, methodology): “(Nilsback and Zisserman 2008) and (Chai et al. 2011, 2012) introduce techniques that improve ROI for feature extraction by simultaneously segmenting and recognizing FGVCs.”
Efficient Object Detection and Segmentation for Fine-Grained Recognition (2013, background): “Therefore, developing automated recognition systems for such tasks is of much benefit to non-experts.”
Segmentation Propagation in ImageNet (2012, unknown): “Foreground-background segmentation is the fundamental task of producing a binary segmentation of an image, separating the foreground object from the background [1, 3, 4].”
TriCoS: A Tri-level Class-Discriminative Co-segmentation Method for Image Classification (2012, methodology): “The overall pipeline can be seen as an improved version of [6].”