Style Transfer

The DukeMTMC-reID (Duke Multi-Tracking Multi-Camera ReIDentification) dataset is a subset of the DukeMTMC for image-based person re-ID. The dataset is created from high-resolution videos from 8 different cameras. It is one of the largest pedestrian image datasets wherein images are cropped by hand-drawn bounding boxes. The dataset consists 16,522 training images of 702 identities, 2,228 query images of the other 702 identities and 17,661 gallery images. NOTE: This dataset has been retracted. Source: Deep Co-attention based Comparators For Relative Representation Learning in Person Re-identification

MPI (Max Planck Institute) Sintel is a dataset for optical flow evaluation that has 1064 synthesized stereo images and ground truth data for disparity. Sintel is derived from open-source 3D animated short film Sintel. The dataset has 23 different scenes. The stereo images are RGB while the disparity is grayscale. Both have resolution of 1024×436 pixels and 8-bit per channel. Source: Fast Disparity Estimation using Dense Networks*

Grammarly’s Yahoo Answers Formality Corpus (GYAFC) is the largest dataset for any style containing a total of 110K informal / formal sentence pairs. Yahoo Answers is a question answering forum, contains a large number of informal sentences and allows redistribution of data. The authors used the Yahoo Answers L6 corpus to create the GYAFC dataset of informal and formal sentence pairs. In order to ensure a uniform distribution of data, they removed sentences that are questions, contain URLs, and are shorter than 5 words or longer than 25. After these preprocessing steps, 40 million sentences remain. The Yahoo Answers corpus consists of several different domains like Business, Entertainment & Music, Travel, Food, etc. Pavlick and Tetreault formality classifier (PT16) shows that the formality level varies significantly across different genres. In order to control for this variation, the authors work with two specific domains that contain the most informal sentences and show results on training and testing within those categories. The authors use the formality classifier from PT16 to identify informal sentences and train this classifier on the Answers genre of the PT16 corpus which consists of nearly 5,000 randomly selected sentences from Yahoo Answers manually annotated on a scale of -3 (very informal) to 3 (very formal). They find that the domains of Entertainment & Music and Family & Relationships contain the most informal sentences and create the GYAFC dataset using these domains. Source: Dear Sir or Madam, May I Introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer

WikiArt contains painting from 195 different artists. The dataset has 42129 images for training and 10628 images for testing. Source: Adding New Tasks to a Single Network with Weight Transformations using Binary Masks Image Source: https://towardsdatascience.com/the-non-treachery-of-dataset-df1f6cbe577e

MPIIGaze is a dataset for appearance-based gaze estimation in the wild. It contains 213,659 images collected from 15 participants during natural everyday laptop use over more than three months. It has a large variability in appearance and illumination. Source: https://www.mpi-inf.mpg.de/departments/computer-vision-and-machine-learning/research/gaze-based-human-computer-interaction/appearance-based-gaze-estimation-in-the-wild Image Source: https://www.mpi-inf.mpg.de/departments/computer-vision-and-machine-learning/research/gaze-based-human-computer-interaction/appearance-based-gaze-estimation-in-the-wild

POP909 is a dataset which contains multiple versions of the piano arrangements of 909 popular songs created by professional musicians. The main body of the dataset contains the vocal melody, the lead instrument melody, and the piano accompaniment for each song in MIDI format, which are aligned to the original audio files. Furthermore, annotations are provided of tempo, beat, key, and chords, where the tempo curves are hand-labelled and others are done by MIR algorithms. Source: https://arxiv.org/pdf/2008.07142.pdf Image Source: https://arxiv.org/pdf/2008.07142.pdf

The iKala dataset is a singing voice separation dataset that comprises of 252 30-second excerpts sampled from 206 iKala songs (plus 100 hidden excerpts reserved for MIREX data mining contest). The music accompaniment and the singing voice are recorded at the left and right channels respectively. Additionally, the human-labeled pitch contours and timestamped lyrics are provided. This dataset is not available anymore. Source: http://mac.citi.sinica.edu.tw/ikala/ 1. "T" as a sofa: The "T" horizontal strip can mimic the back of a sofa with a delicate cushion or details of the uphols or appliances with the color button. The "T" vertical strip can show a feet or arm of the sofa, shiny, yet firm. 2. Merge "P": Put "P" next to "T", your curve to delicately with the top "T." It is intertwined. The circular part of "P" can show a cushion or a curved chair and synchronize the subject of furniture. Make sure "P" is visually relying on "T", which reflects the relationship of cohesion and balance. 5. Coherence of "B" and "I": "B" can be aligned as a pair of cushions or a modern chair, with mild curves with glossy and modern aesthetics. "I" can be a symbol of a shiny furniture or a vertical light bar and completes the shapes without overburdess them. Color palette 4: Includes soft soil colors such as beige, top and gray shades, along with silent or silver gold tips to touch elegance. Consider a slope effect to enhance modernity, to keep colors elegant and complex. 5. Connect the letters: Use the overlap or intertwined edges that the letters meet for the symbol of unity. The plan should allow viewers to distinguish each letter while feeling part of the same "structure". 6. Background patterns: Use delicate geometric patterns or textures that mimic fabrics or furniture materials such as wood seeds or woven fibers. These patterns must remain minimalist and focus on highlighting the logo, while maintaining communication. While it deals with the subject of furniture and design, this concept conveys modernity, creativity and professional. If you like, I can create a draft design for better visualization.

Touchdown is a corpus for executing navigation instructions and resolving spatial descriptions in visual real-world environments. The task is to follow instruction to a goal position and there find a hidden object, Touchdown the bear. Source: https://github.com/lil-lab/touchdown Image Source: https://github.com/lil-lab/touchdown

An annotated image memorability dataset to date (with 60,000 labeled images from a diverse array of sources). Source: Understanding and Predicting Image Memorability at a Large Scale

TextSeg is a large-scale fine-annotated and multi-purpose text detection and segmentation dataset, collecting scene and design text with six types of annotations: word- and character-wise bounding polygons, masks and transcriptions. Source: https://github.com/SHI-Labs/Rethinking-Text-Segmentation Image Source: https://github.com/SHI-Labs/Rethinking-Text-Segmentation

A new dataset of handwritten text with fine-grained annotations at the character level and report results from an initial user evaluation. Source: DeepWriting: Making Digital Ink Editable via Deep Generative Modeling

To comprehensively evaluate the effectiveness and generalization ability of style transfer methods, we build StyleBench that covers 73 distinct styles, ranging from paintings, flat illustrations, 3D rendering to sculptures with varying materials. For each style, we collect 5-7 distinct images with variations. In total, our StyleBench contains 490 images across diverse styles.

We construct a style-balanced dataset, called StyleGallery, covering several open source datasets. Specifically, StyleGallery includes JourneyDB, a dataset comprising a broad spectrum of diverse styles derived from MidJourney, and WIKIART, with extensive fine-grained painting styles, such as pointillism and ink drawing, and a subset of stylized images from LAION-Aesthetics.

The scarcity of high-quality and multi-task singing datasets significantly hinders the development of diverse controllable and personalized singing tasks, as existing singing datasets suffer from low quality, limited diversity of languages and singers, absence of multi-technique information and realistic music scores, and poor task suitability. To tackle these problems, we present GTSinger, a large Global, multi-Technique, free-to-use, high-quality singing corpus with realistic music scores, designed for all singing tasks, along with its benchmarks. Particularly, (1) we collect 80.59 hours of high-quality songs, forming the largest recorded singing dataset; (2) 20 professional singers across nine languages offer diverse timbres and styles; (3) we provide controlled comparison and phoneme-level annotations of six singing techniques, helping technique modeling and control; (4) GTSinger offers realistic music scores, assisting real-world musical composition; (5) singing voices are accompanied by manual phoneme-to-audio alignments, global style labels, and 16.16 hours of paired speech for various singing tasks. Moreover, to facilitate the use of GTSinger, we conduct four benchmark experiments: technique-controllable singing voice synthesis, technique recognition, style transfer, and speech-to-singing conversion.

Aims to facilitate research in caricature recognition. All the caricatures and face images were collected from the Web. Compared with two existing datasets, this dataset is much more challenging, with a much greater number of available images, artistic styles and larger intra-personal variations. Source: WebCaricature: a benchmark for caricature recognition

PASTEL is a parallelly annotated stylistic language dataset. The dataset consists of ~41K parallel sentences and 8.3K parallel stories annotated across different personas. Source: PASTEL

A new text effects dataset with 141,081 text effect/glyph pairs in total. The dataset consists of 152 professionally designed text effects rendered on glyphs, including English letters, Chinese characters, and Arabic numerals. Source: TE141K: Artistic Text Benchmark for Text Effect Transfer

The Chinese Traditional Painting dataset for style transfer contains 1000 content images and 100 style images. The content images are mostly the photorealistic scenes of mountain, lake, river, bridge, and buildings in regions south of the Yangtze River. It includes not only the scenes of China, but also beautiful pictures of Rhine, Alps, Yellow Stone, Grand Canyon, etc. The content images include diverse types of Chinese traditional paintings. Source: https://github.com/lbsswu/Chinese_style_transfer Image Source: https://github.com/lbsswu/Chinese_style_transfer

Gutenberg Poem Dataset is used for the next verse prediction component. Source: Gutenberg Poem Dataset

Image dataset with about 600K Flickr photos. Source: Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks

A high-resolution version of VGGFace2 for academic face editing purposes. This project uses GFPGAN for image restoration and insightface for data preprocessing (crop and align).

CodeSCAN is the first large-scale and diverse dataset of coding screenshots with pixel-perfect annotations. It features: - 24 popular programming languages (according to Github) - 100 random repositories per language (with MIT, BSD-3 or WTFPL License), i.e. 2.400 repositories in total - Per repository we use 5 files, i.e. 12.000 files in total - ~100 different themes and 25 different fonts - Diverse layouts changes, such as menu bar visibility, sidebar position, output window content, etc. - Numerous realistic interactions such as searching, typing and selecting within a file, etc. Check our project page (https://a-nau.github.io/codescan/) for details.

This is a dataset of 18624 fonts labeled as 100% Free and Public domain / GPL / OFL on https://www.dafont.com/ with .ttf and .otf extensions. Code used to create it can be found at: https://github.com/duskvirkus/dafonts-free