Conditional Image Generation

The COCO (Common Objects in Context) dataset is a large-scale object detection, segmentation, and captioning dataset. It is designed to encourage research on a wide variety of object categories and is commonly used for benchmarking computer vision models. It is an essential dataset for researchers and developers working on object detection, segmentation, and pose estimation tasks.

The CIFAR-100 dataset (Canadian Institute for Advanced Research, 100 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. There are 600 images per class. Each image comes with a "fine" label (the class to which it belongs) and a "coarse" label (the superclass to which it belongs). There are 500 training images and 100 testing images per class. The criteria for deciding whether an image belongs to a class were as follows: * The class name should be high on the list of likely answers to the question “What is in this picture?” * The image should be photo-realistic. Labelers were instructed to reject line drawings. * The image should contain only one prominent instance of the object to which the class refers. * The object may be partially occluded or seen from an unusual viewpoint as long as its identity is still clear to the labeler. Source: https://www.cs.toronto.edu/~kriz/cifar.html Image Source: https://www.cs.toronto.edu/~kriz/cifar.html

Tiny ImageNet contains 100000 images of 200 classes (500 for each class) downsized to 64×64 colored images. Each class has 500 training images, 50 validation images and 50 test images. Source: Embedded Encoder-Decoder in Convolutional Networks Towards Explainable AI Image Source: https://arxiv.org/pdf/1707.08819.pdf

ImageNet Long-Tailed is a subset of /dataset/imagenet dataset consisting of 115.8K images from 1000 categories, with maximally 1280 images per class and minimally 5 images per class. The additional classes of images in ImageNet-2010 are used as the open set. Source: Large-Scale Long-Tailed Recognition in an Open World

CelebAMask-HQ is a large-scale face image dataset that has 30,000 high-resolution face images selected from the CelebA dataset by following CelebA-HQ. Each image has segmentation mask of facial attributes corresponding to CelebA. Source: https://github.com/switchablenorms/CelebAMask-HQ Image Source: https://github.com/switchablenorms/CelebAMask-HQ

We introduce ArtBench-10, the first class-balanced, high-quality, cleanly annotated, and standardized dataset for benchmarking artwork generation. It comprises 60,000 images of artwork from 10 distinctive artistic styles, with 5,000 training images and 1,000 testing images per style. ArtBench-10 has several advantages over previous artwork datasets. Firstly, it is class-balanced while most previous artwork datasets suffer from the long tail class distributions. Secondly, the images are of high quality with clean annotations. Thirdly, ArtBench-10 is created with standardized data collection, annotation, filtering, and preprocessing procedures. We provide three versions of the dataset with different resolutions (32×32, 256×256, and original image size), formatted in a way that is easy to be incorporated by popular machine learning frameworks.

Human-Art is a versatile human-centric dataset to bridge the gap between natural and artificial scenes. It includes twenty high-quality human scenes, including natural and artificial humans in both 2D representation and 3D representation. It includes 50,000 images including more than 123,000 human figures in 20 scenarios, with annotations of human bounding box, 21 2D human keypoints, human self-contact keypoints, and description text.

It is composed of around 770k of color 256x256 RGB images extracted from the European Union Intellectual Property Office (EUIPO) open registry. Each of them is associated to multiple labels that classify the figurative and textual elements that appear in the images. These annotations have been classified by the EUIPO evaluators using the Vienna classification, a hierarchical classification of figurative marks. We suggest it to be used for: 1. Unconditional trademark generation 2. Conditional trademark generation. 3. Multi-label logo classification (Vienna classification). 4. Optical Character Recognition. 5. Conditional trade 6. Image segmentation. 7. Image retrieval

To evaluate our proposed strategy of asynchronous communication for LLMs, we run games of Mafia with human players, incorporating an LLM-based agent as an additional player, within an asynchronous chat environment. The data includes all players messages and votes including timestamps, game management messages (e.g., announcements of the beginning and end of phases), in addition to records related to the agent, such as the prompts that were provided at each timestamp. While prior datasets for Mafia exist, ours is the first to integrate an LLM agent, allowing analysis of human-LLM interactions. Detailed analysis of the data is available in our paper: Time to Talk: LLM Agents for Asynchronous Group Communication in Mafia Games

A new SIS benchmark designed to assess generation performance under noisy conditions, simulating human error that can occur during real-world applications. [DS] employs downsampled semantic maps that are resized by nearest-neighbor interpolation, simulating human errors e.g., jagged edges and coarse/low resolution user inputs.

A new SIS benchmark designed to assess generation performance under noisy conditions, simulating human error that can occur during real-world applications. [Edge] masks the edges of instances with an unlabeled class, imitating incomplete annotations around edges, especially between instances.

A new SIS benchmark designed to assess generation performance under noisy conditions, simulating human error that can occur during real-world applications. [Random] randomly adds an unlabeled class to the semantic maps, mimicing unintended user error and extreme random noise.