190 datasets found

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used a large-scale text-to-image model to generate images with multiple subjects.

The proposed model is built on the theoretical analysis presented in [30,14]. Similar to the ARAE [14], our model provides flexibility in learning an autoencoder from the input...

Dysca is a dynamic and scalable benchmark for evaluating the perception ability of Large Vision-Language Models (LVLMs) via various subtasks and scenarios.

MARS is an innovative auto-regressive framework that not only retains the capabilities of pre-trained Large Language Models (LLMs) but also incorporates top-tier text-to-image...

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used the two moons dataset for experiments.

The dataset used in the paper is DPM-Solver++

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used a pre-trained model to generate images.

This dataset is used for training and testing the BigGAN model.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors analyzed the generative mechanism of diffusion models and proposed a novel method...

Image generation from scene graphs is a task in computer vision that requires generating images from graph-structured inputs, such as scene graphs.

Generating images from graph-structured inputs, such as scene graphs, is uniquely challenging due to the difficulty of aligning nodes and connections in graphs with objects and...

A text-to-image diffusion model trained on a recaptioned dataset to improve image generation quality and semantic alignment.

A Two-stage Personalized Virtual Try-on Framework with Shape Control and Texture Guidance

The dataset used for language modeling, disentangled representation learning, and image generation.

The dataset used in this paper is Bedroom, a dataset of 128 × 128 Bedroom images.

The dataset used in this paper is Horse, a dataset of 128 × 128 Horse images.

Relational CLEVR is a synthetic dataset of rendered 3D objects of various colours, shapes, sizes and textures.

Positional CLEVR is a synthetic dataset of rendered 3D objects of various colours, shapes, sizes and textures.

The dataset used in the paper is not explicitly described, but it is mentioned that it is a generative model dataset.

Generative Adversarial Networks (GANs) have a great performance in image generation, but they need a large scale of data to train the entire framework, and often result in...