Elizaveta Petrova

Karen Efremyan, Elizaveta Petrova, Evgeny Kaskov et al.

A common problem for composite images is the incompatibility of their foreground and background components. Image harmonization aims to solve this problem, making the whole image look more authentic and coherent. Most existing solutions predict lookup tables (LUTs) or reconstruct images, utilizing various attributes of composite images. Recent approaches have primarily focused on employing global transformations like normalization and color curve rendering to achieve visual consistency, and they often overlook the importance of local visual coherence. We present a patch-based harmonization network consisting of novel Patch-based normalization (PN) blocks and a feature extractor based on statistical color transfer. Extensive experiments demonstrate the network's high generalization capability for different domains. Our network achieves state-of-the-art results on the iHarmony4 dataset. Also, we created a new human portrait harmonization dataset based on FFHQ and checked the proposed method to show the generalization ability by achieving the best metrics on it. The benchmark experiments confirm that the suggested patch-based normalization block and feature extractor effectively improve the network's capability to harmonize portraits. Our code and model baselines are publicly available.

Karina Kvanchiani, Petr Surovtsev, Alexander Nagaev et al.

This paper investigates the recognition of the Russian fingerspelling alphabet, also known as the Russian Sign Language (RSL) dactyl. Dactyl is a component of sign languages where distinct hand movements represent individual letters of a written language. This method is used to spell words without specific signs, such as proper nouns or technical terms. The alphabet learning simulator is an essential isolated dactyl recognition application. There is a notable issue of data shortage in isolated dactyl recognition: existing Russian dactyl datasets lack subject heterogeneity, contain insufficient samples, or cover only static signs. We provide Bukva, the first full-fledged open-source video dataset for RSL dactyl recognition. It contains 3,757 videos with more than 101 samples for each RSL alphabet sign, including dynamic ones. We utilized crowdsourcing platforms to increase the subject's heterogeneity, resulting in the participation of 155 deaf and hard-of-hearing experts in the dataset creation. We use a TSM (Temporal Shift Module) block to handle static and dynamic signs effectively, achieving 83.6% top-1 accuracy with a real-time inference with CPU only. The dataset, demo code, and pre-trained models are publicly available.

Karina Kvanchiani, Elizaveta Petrova, Karen Efremyan et al.

Recently, video conferencing apps have become functional by accomplishing such computer vision-based features as real-time background removal and face beautification. Limited variability in existing portrait segmentation and face parsing datasets, including head poses, ethnicity, scenes, and occlusions specific to video conferencing, motivated us to create a new dataset, EasyPortrait, for these tasks simultaneously. It contains 40,000 primarily indoor photos repeating video meeting scenarios with 13,705 unique users and fine-grained segmentation masks separated into 9 classes. Inappropriate annotation masks from other datasets caused a revision of annotator guidelines, resulting in EasyPortrait's ability to process cases, such as teeth whitening and skin smoothing. The pipeline for data mining and high-quality mask annotation via crowdsourcing is also proposed in this paper. In the ablation study experiments, we proved the importance of data quantity and diversity in head poses in our dataset for the effective learning of the model. The cross-dataset evaluation experiments confirmed the best domain generalization ability among portrait segmentation datasets. Moreover, we demonstrate the simplicity of training segmentation models on EasyPortrait without extra training tricks. The proposed dataset and trained models are publicly available.

Karina Kvanchiani, Roman Kraynov, Elizaveta Petrova et al.

Accurate recognition and interpretation of sign language are crucial for enhancing communication accessibility for deaf and hard of hearing individuals. However, current approaches of Isolated Sign Language Recognition (ISLR) often face challenges such as low data quality and variability in gesturing speed. This paper introduces a comprehensive model training pipeline for ISLR designed to accommodate the distinctive characteristics and constraints of the Sign Language (SL) domain. The constructed pipeline incorporates carefully selected image and video augmentations to tackle the challenges of low data quality and varying sign speeds. Including an additional regression head combined with IoU-balanced classification loss enhances the model's awareness of the gesture and simplifies capturing temporal information. Extensive experiments demonstrate that the developed training pipeline easily adapts to different datasets and architectures. Additionally, the ablation study shows that each proposed component expands the potential to consider ISLR task specifics. The presented strategies enhance recognition performance across various ISLR benchmarks and achieve state-of-the-art results on the WLASL and Slovo datasets.

Evgeny Kaskov, Elizaveta Petrova, Petr Surovtsev et al.

Homonyms are words with identical spelling but distinct meanings, which pose challenges for many generative models. When a homonym appears in a prompt, diffusion models may generate multiple senses of the word simultaneously, which is known as homonym duplication. This issue is further complicated by an Anglocentric bias, which includes an additional translation step before the text-to-image model pipeline. As a result, even words that are not homonymous in the original language may become homonyms and lose their meaning after translation into English. In this paper, we introduce a method for measuring duplication rates and conduct evaluations of different diffusion models using both automatic evaluation utilizing Vision-Language Models (VLM) and human evaluation. Additionally, we investigate methods to mitigate the homonym duplication problem through prompt expansion, demonstrating that this approach also effectively reduces duplication related to Anglocentric bias. The code for the automatic evaluation pipeline is publicly available.

Alexander Kapitanov, Karina Kvanchiani, Alexander Nagaev et al.

One of the main challenges of the sign language recognition task is the difficulty of collecting a suitable dataset due to the gap between hard-of-hearing and hearing societies. In addition, the sign language in each country differs significantly, which obliges the creation of new data for each of them. This paper presents the Russian Sign Language (RSL) video dataset Slovo, produced using crowdsourcing platforms. The dataset contains 20,000 FullHD recordings, divided into 1,000 classes of isolated RSL gestures received by 194 signers. We also provide the entire dataset creation pipeline, from data collection to video annotation, with the following demo application. Several neural networks are trained and evaluated on the Slovo to demonstrate its teaching ability. Proposed data and pre-trained models are publicly available.