3D Object Representations for Fine-Grained Categorization

By Jonathan Krause, Michael Stark, Jia Deng, Li Fei-Fei. 2013

4,905 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

Fixing the train-test resolution discrepancy (2019, methodology): “We evaluated it on the following datasets: Stanford Cars [16], CUB-200-2011 [36], Oxford 102 Flowers [23], Oxford-IIIT Pets [25] 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”
Fine-Grained Image Classification via Combining Vision and Language (2017, background): “domains have been covered, such as bird types [6, 7], dog species [8], plant breeds [9] , car types [10] and aircraft models [11].”
Training data-efficient image transformers & distillation through attention (2020, neutral): “• Our models pre-learned on Imagenet are competitive when transferred to different downstream tasks such as fine-grained classification, on several popular public benchmarks: CIFAR-10, CIFAR-100, Flowers, Stanford Cars and iNaturalist-18/19.”
Grafit: Learning fine-grained image representations with coarse labels (2020, methodology): “Flowers-102 [41] 2,040 6,149 102 Stanford Cars [35] 8,144 8,041 196 Food101 [7] 75,750 25,250 101”
Projected GANs Converge Faster (2021, methodology): “We compare on LSUN cat and horse [84], ADE indoor (a subset of ADE [91] proposed in [3]), the full Oxford ﬂowers dataset with 8k images [56], KITTI-ﬁsheye (a subset of KITTI-360 [46], consisting of ﬁsheye images), STL-10 [8], CUB200 [80], Stanford Dogs [36], and Stanford Cars [42].”
CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP (2021, methodology): “Stanford Cars (Krause et al., 2013) (Ilharco et al., 2021), we include the original CLIP results (Radford et al., 2021, Table 12).”
Unsupervised Embedding Learning via Invariant and Spreading Instance Feature (2019, methodology): “We follow the experimental settings described in [32] to conduct experiments on CUB200-2011(CUB200) [43], Stanford Online Product (Product) [32] and Car196 [22] datasets.”
Semantic Image Inpainting with Deep Generative Models (2016, methodology): “We evaluate our method on three dataset: the CelebFaces Attributes Dataset (CelebA) [23], the Street View House Numbers (SVHN) [29] and the Stanford Cars Dataset [17].”
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time (2022, methodology): “, 2016), Stanford Cars (Krause et al., 2013) and ImageNet (Deng et al.”
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision (2021, background): “, 2012), Stanford Cars (Krause et al., 2013), and Food101 (Bossard et al.”
Extreme Clicking for Efficient Object Annotation (2017, background): “A qualification test is a good mechanism for enhancing the quality of crowd-sourcing data and for filtering out bad annotators and spammers [1, 19, 33, 37].”
On Calibration of Modern Neural Networks (2017, background): “Scaling Vector Scaling Matrix Scaling Birds ResNet 50 22.54% 55.02% 23.37% 37.76% 22.54% 22.99% 29.51% Cars ResNet 50 14.28% 16.24% 14.9% 19.25% 14.28% 14.15% 17.98% CIFAR-10 ResNet 110 6.21% 6.45% 6.36% 6.25% 6.21% 6.37% 6.42% CIFAR-10 ResNet 110 (SD) 5.64% 5.59% 5.62% 5.55% 5.64% 5.62% 5.69% CIFAR”
AutoHAS: Differentiable Hyper-parameter and Architecture Search (2020, background): “We demonstrate the effectiveness of our AutoHAS on five vision datasets, i.e., ImageNet [7], Birdsnap [3], CIFAR-10, CIFAR-100 [29], and Cars [28], and a NLP dataset, i.e., SQuAD 1.1 [39].”