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MonoDiffusion: Self-Supervised Monocular Depth Estimation Using Diffusion Model
MonoDiffusion: A novel self-supervised monocular depth estimation framework by reformulating it as an iterative denoising process. -
InsPLAD: A Dataset and Benchmark for Power Line Asset Inspection in UAV Images
Power line maintenance and inspection are essential to avoid power supply interrup- tions, reducing its high social and financial impacts yearly. Automating power line visual... -
METER: a mobile vision transformer architecture for monocular depth estimation
Monocular depth estimation is a fundamental knowledge for autonomous systems that need to assess their own state and perceive the surrounding environment. -
Omniglot dataset
The Omniglot dataset consists of 100 classes, each containing 20 images. Ten images were taken from each class for augmentation, and the rest were used as the test set. Each... -
ImageNet with CMA-Search
A dataset of ImageNet images with subtle 3D perspective changes that can break ImageNet-trained classification networks. -
Controlled Rendered Data of Real World Objects
A dataset of complex image data with a fixed, known distribution, generated using a computer graphics pipeline. -
TinyImageNet
The dataset used for the experiments of the paper "CORE-PERIPHERY PRINCIPLE GUIDED REDISIGN OF SELF-ATTENTION IN TRANSFORMERS" -
Blender Dataset
The Blender dataset consists of 8 synthetic 3D scenes, each with a hundred posed images of resolution 800 × 800. -
ASL Dataset
ASL dataset for finger spelling, containing 26 signs for letters A to Z, with 3 additional signs, and 87000 samples. -
ResNetX: a more disordered and deeper network architecture
Image classification results on CIFAR-10 and CIFAR-100 benchmarks suggested that our new network architecture performs better than ResNet. -
TinyImagenet dataset
The dataset used in the paper is not explicitly described, but it is mentioned that the authors used TinyImagenet dataset for pre-training the embedding functions. -
CIFAR-10, CIFAR-100, and STL-10 datasets
The dataset used in the paper is not explicitly described, but it is mentioned that the authors used CIFAR-10, CIFAR-100, and STL-10 datasets for training and testing the... -
IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for Zero-Sho...
Learning to synthesize data has emerged as a promising direction in zero-shot quantization (ZSQ), which represents neural networks by low-bit integer without accessing any of... -
Surface Networks
The dataset used in the paper is a 3D mesh dataset, which is used for training and testing the Surface Networks model. -
Neural 3D Mesh Renderer
The dataset used in the paper Neural 3D Mesh Renderer. The dataset consists of 3D models of objects. -
Simulated Multiview Lidar Dataset
Simulated multiview lidar dataset for transient neural radiance fields -
Generative Adversarial Networks
Generative Adversarial Networks (GANs) consist of two networks: a generator G(z) and a discriminator D(x). The discriminator is trying to distinguish real objects from objects... -
PASCAL Context
The PASCAL Context dataset is a benchmark for multi-task learning in computer vision. It contains 10103 images with 5 tasks: semantic segmentation, human body part segmentation,...
