American and Russian Sign Language Dactyl Recognition and Text2Sign Translation

Makarov, Ilya; Veldyaykin, Nikolay; Chertkov, Maxim; Pokoev, Aleksei

doi:10.1007/978-3-030-37334-4_28

Cited by 7 publications

(1 citation statement)

References 20 publications

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“…Consequently, the number of logo classes was fixed. Deep learning models for object detection, such as Faster R-CNN [2], have demonstrated robust performance in this domain [3]- [5] as well as in many others [6]- [10].…”

Section: Introductionmentioning

confidence: 99%

Scalable Zero-Shot Logo Recognition

Shulgin,

Makarov

2023

IEEE Access

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Brand logo recognition is a task focused on the identification and classification of logos, with various applications such as brand protection and market discovery. Since the number of brands is dynamic and evolves over time, we propose a two-step, zero-shot framework for this problem. In the first step, we train a Scaled-YOLOv4 single-stage universal logo detector to identify regions containing logolike objects. Our results indicate that this detector achieves generalizability comparable to the two-stage Faster-RCNN model. In the second step, we employ an enhanced CLIP model for zero-shot classification of the identified regions. Our experiments demonstrate that the CLIP model outperforms state-of-the-art fewshot classifiers in terms of accuracy. Additionally, we adopt test-time augmentation to improve the model's resistance against false positives. We also present a proof-of-concept for fine-tuning the CLIP model, which enhances its cosine similarity measures. Our proposed end-to-end solution is scalable in terms of the number of brands and requires only the brand names for detection. The logo detector achieves superior performance on the FlickrLogos-32 dataset without the need for additional fine-tuning.INDEX TERMS universal logo detector, few-shot learning, logo recognition, Flickr-32

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Section: Introductionmentioning

confidence: 99%

Scalable Zero-Shot Logo Recognition

Shulgin,

Makarov

2023

IEEE Access

Self Cite

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Learning Facial Expression Recognition In-the-Wild from Synthetic Data Based on an Ensemble of Lightweight Neural Networks

Nguyen,

Savchenko

2024

Communications in Computer and Information Science

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Machine translation from signed to spoken languages: state of the art and challenges

Coster

Shterionov

Herreweghe

et al. 2023

Univ Access Inf Soc

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Automatic translation from signed to spoken languages is an interdisciplinary research domain on the intersection of computer vision, machine translation (MT), and linguistics. While the domain is growing in terms of popularity—the majority of scientific papers on sign language (SL) translation have been published in the past five years—research in this domain is performed mostly by computer scientists in isolation. This article presents an extensive and cross-domain overview of the work on SL translation. We first give a high level introduction to SL linguistics and MT to illustrate the requirements of automatic SL translation. Then, we present a systematic literature review of the state of the art in the domain. Finally, we outline important challenges for future research. We find that significant advances have been made on the shoulders of spoken language MT research. However, current approaches often lack linguistic motivation or are not adapted to the different characteristics of SLs. We explore challenges related to the representation of SL data, the collection of datasets and the evaluation of SL translation models. We advocate for interdisciplinary research and for grounding future research in linguistic analysis of SLs. Furthermore, the inclusion of deaf and hearing end users of SL translation applications in use case identification, data collection, and evaluation, is of utmost importance in the creation of useful SL translation models.

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American and Russian Sign Language Dactyl Recognition and Text2Sign Translation

Cited by 7 publications

References 20 publications

Scalable Zero-Shot Logo Recognition

Scalable Zero-Shot Logo Recognition

Learning Facial Expression Recognition In-the-Wild from Synthetic Data Based on an Ensemble of Lightweight Neural Networks

Machine translation from signed to spoken languages: state of the art and challenges

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