Cat Head Detection - How to Effectively Exploit Shape and Texture Features

By Weiwei Zhang, Xiaoou Tang. 2008

206 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

GAN Memory with No Forgetting (2020, methodology): “1 GAN memory on a stream of generation tasks To demonstrate the superiority of our GAN memory over existing replay-based methods, we design a challenging lifelong generation problem consisting of 6 perceptually-distant tasks/datasets (see Figure 5): Flowers [57], Cathedrals [99], Cats [97], Brain-tu”
The relativistic discriminator: a key element missing from standard GAN (2018, methodology): “Experiments were conducted on the CIFAR-10 dataset [Krizhevsky, 2009] and the CAT dataset [Zhang et al., 2008].”
GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution (2020, unknown): “2681 Cat [43] 17.”
Unsupervised Learning of Probably Symmetric Deformable 3D Objects From Images in the Wild (2019, background): “4 we show reconstruction results of human faces from CelebA and 3DFAW, cat faces from [66, 42] and synthetic cars from ShapeNet.”
Unsupervised Learning of Object Landmarks by Factorized Spatial Embeddings (2017, methodology): “We train landmark detectors on 49525 shoes from the UT Zappos50k dataset of [68] and 8609 images from the cat heads dataset of [72] and keep the rest for validation.”
Unsupervised Discovery of Object Landmarks as Structural Representations (2018, methodology): “We evaluate our method on a variety of datasets, including CelebA [31] and AFLW [35] for human faces, the cat head dataset [79], a car dataset built from PASCAL 3D [69], shoe images from UT Zappos50k [74], human pose images from Human3.”
On Positive-Unlabeled Classification in GAN (2020, methodology): “In this section, we evaluate the proposed method on a range of datasets including MNIST [14], FMNIST [22], CIFAR-10 [13], CAT [25], and LSUN-bedroom [24].”
Layout2image: Image Generation from Layout (2020, background): “However, existing text-to-image approaches exhibit two drawbacks: (i) most approaches can only generate plausible results on simple datasets such as cats (Zhang et al. 2008), birds (Welinder et al.”
NeRF: Neural Radiance Field in 3D Vision, A Comprehensive Review (2022, unknown): “The generator was a SIREN-based [114] NeRF volumetric renderer, with sinusoidal activation replacing the standard ReLU activation in the density and color MLPs. π -GAN outperformed GRAF[62] and HOLOGAN on standard GAN datasets such as Celeb-A[115], CARLA [116] and Cats [117].”
Connections between Support Vector Machines, Wasserstein distance and gradient-penalty GANs (2019, methodology): “Furthermore, we ran experiments on CAT (Zhang et al., 2008) with 1) Standard CNN (in 32x32), and 2) DCGAN (in 64x64).”
DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort (2021, methodology): “We select images for these small test datasets from Stanford Cars [28], Celeb-A mask [35], CUB [52], and Kaggle Cat [53], respectively.”
Gradient penalty from a maximum margin perspective. (2020, methodology): “Furthermore, we ran experiments on CAT (Zhang et al., 2008) with 1) Standard CNN (in 32x32), and 2) DCGAN (in 64x64).”
Building high-level features using large scale unsupervised learning (2011, background): “scribed in (Zhang et al., 2008).”
HoloGAN: Unsupervised Learning of 3D Representations From Natural Images (2019, methodology): “Data We train HoloGAN using a variety of datasets: Basel Face [42], CelebA [35], Cats [63], Chairs [7], Cars [58], and LSUN bedroom [59].”
Integral Channel Features (2009, background): “In many of the above mentioned works the focus was on the learning aspect [7, 8, 24] or novel applications [2, 6, 25] and it is difficult to decouple the performance gains due to the richer features from gains due to more powerful learning methods.”
On the Effectiveness of Least Squares Generative Adversarial Networks (2017, methodology): “We further evaluate LSGANs on a cat dataset [44].”
Do 2D GANs Know 3D Shape? Unsupervised 3D shape reconstruction from 2D Image GANs (2020, methodology): “, 2009), a combined cat dataset (Zhang et al., 2008; Parkhi et al., 2012), LSUN Car (Yu et al.”
Image Generation From Layout (2018, background): “However, existing text-to-image approaches exhibit two drawbacks: (i) most approaches can only generate plausible results on simple datasets such as cats [49], birds [44] or flowers [30].”