Care of aged doctors

By Omkar M. Parkhi, Andrea Vedaldi, Andrew Zisserman, C. V. Jawahar. 1969

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Citations in the corpus

Learning Transferable Visual Models From Natural Language Supervision (2021, background): “We had five different humans look at each of 3669 images in the test split of the Oxford IIT Pets dataset (Parkhi et al., 2012) and select which of the 37 cat or dog breeds best matched the image (or ‘I don’t know’ if they were completely uncertain).”
Do Better ImageNet Models Transfer Better? (2018, background): “These datasets covered a wide range of image classification tasks, including superordinate-level object classification (CIFAR-10 [37], CIFAR-100 [37], PASCAL VOC 2007 [19], Caltech-101 [20]); fine-grained object classification (Food-101 [5], Birdsnap [4], Stanford Cars [36], FGVC Aircraft [48], Oxfo”
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (2019, neutral): “(Maji et al., 2013) 6,667 3,333 100 Oxford-IIIT Pets (Parkhi et al., 2012) 3,680 3,369 37 Food-101 (Bossard et al., 2014) 75,750 25,250 101”
On Robustness and Transferability of Convolutional Neural Networks (2020, neutral): “CIFAR-100 [38], DTD [9], Flowers102 [47], Pets [49], Sun397 [68], SVHN [46].”
Big Transfer (BiT): General Visual Representation Learning (2019, methodology): “These tasks include ImageNet’s ILSVRC-2012 [10], CIFAR-10/100 [27], Oxford-IIIT Pet [41], Oxford Flowers-102 [39] (including few-shot variants), and the 1000-sample VTAB-1k benchmark [66], which consists of 19 diverse datasets.”
A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark (2019, background): “, 2014) Natural Flowers102 2,040 102 (Nilsback & Zisserman, 2008) Natural Pets 3,680 37 (Parkhi et al., 2012) Natural Sun397 87,003 397 (Xiao et al.”
Fixing the train-test resolution discrepancy (2019, methodology): “We evaluated it on the following datasets: iNaturalist 2017 [16], Stanford Cars [19], CUB-200-2011 [40], Oxford 102 Flowers [26], Oxford-IIIT Pets [28], NABirds [39] and Birdsnap [4].”
Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning (2020, methodology): “We perform transfer via linear classification and fine-tuning on the same set of datasets as in [8], namely Food-101 dataset [89], CIFAR-10 [78] and CIFAR-100 [78], Birdsnap [90], the SUN397 scene dataset [79], Stanford Cars [91], FGVC Aircraft [92], the PASCAL VOC 2007 classification task [80], the”
An Empirical Study of Training Self-Supervised Vision Transformers (2021, methodology): “We study four datasets used in [15]: CIFAR-10/100 [25], Oxford Flowers-102 [32], and Oxford-IIIT-Pets [34].”
CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP (2021, neutral): “2014), Pets (Parkhi et al., 2012), RESISC45 (Cheng et al.”
Learning to Prompt for Vision-Language Models (2021, neutral): “We observe that a few words are somewhat relevant to the tasks, such as “enjoyed” for Food101, “ﬂuﬀy” and “paw” for OxfordPets, and “pretty” for DTD.”
Scaling Vision Transformers (2021, neutral): “Similar trends on other datasets, Oxford IIIT Pets [27], CIFAR-100 [23], and Caltech-UCSD Birds [46] are presented in the Appendix, Figure 9.”
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision (2021, methodology): “, 2009) as well as a handful of smaller fine-grained classification datasets such as Oxford Flowers-102 (Nilsback & Zisserman, 2008), Oxford-IIIT Pets (Parkhi et al., 2012), Stanford Cars (Krause et al.”
Visual Prompt Tuning (2022, neutral): “800/1000 200 10,000 Caltech101 [43] 102 6,084 DTD [11] 47 1,880 Flowers102 [55] 102 6,149 Pets [57] 37 3,669 SVHN [54] 10 26,032 Sun397 [76] 397 21,750 Patch Camelyon [69] Specialized 2 800/1000 200 32,768 EuroSAT [29] 10 5,400 Resisc45 [10] 45 6,300 Retinopathy [36] 5 42,670 Clevr/count [34]”