Zero-Shot Sign Language Recognition: Can Textual Data Uncover Sign Languages?

We introduce the problem of zero-shot sign language recognition (ZSSLR), where the goal is to leverage models learned over the seen sign class examples to recognize the instances of unseen signs. To this end, we propose to utilize the readily available descriptions in sign language dictionaries as an intermediate-level semantic representation for knowledge transfer. We introduce a new benchmark dataset called ASL-Text that consists of 250 sign language classes and their accompanying textual descriptions. Compared to the ZSL datasets in other domains (such as object recognition), our dataset consists of limited number of training examples for a large number of classes, which imposes a significant challenge. We propose a framework that operates over the body and hand regions by means of 3D-CNNs, and models longer temporal relationships via bidirectional LSTMs. By leveraging the descriptive text embeddings along with these spatio-temporal representations within a zero-shot learning framework, we show that textual data can indeed be useful in uncovering sign languages. We anticipate that the introduced approach and the accompanying dataset will provide a basis for further exploration of this new zero-shot learning problem.Comment: To appear in British Machine Vision Conference (BMVC) 201

Zero-Shot Sign Language Recognition: Can Textual Data Uncover Sign Languages?

We introduce the problem of zero-shot sign language recognition (ZSSLR), where the goal is to leverage models learned over the seen sign class examples to recognize the instances of unseen signs. To this end, we propose to utilize the readily available descriptions in sign language dictionaries as an intermediate-level semantic representation for knowledge transfer. We introduce a new benchmark dataset called ASL-Text that consists of 250 sign language classes and their accompanying textual descriptions. Compared to the ZSL datasets in other domains (such as object recognition), our dataset consists of limited number of training examples for a large number of classes, which imposes a significant challenge. We propose a framework that operates over the body and hand regions by means of 3D-CNNs, and models longer temporal relationships via bidirectional LSTMs. By leveraging the descriptive text embeddings along with these spatio-temporal representations within a zero-shot learning framework, we show that textual data can indeed be useful in uncovering sign languages. We anticipate that the introduced approach and the accompanying dataset will provide a basis for further exploration of this new zero-shot learning problem

Red Carpet to Fight Club: Partially-supervised Domain Transfer for Face Recognition in Violent Videos

In many real-world problems, there is typically a large discrepancy between the characteristics of data used in training versus deployment. A prime example is the analysis of aggression videos: in a criminal incidence, typically suspects need to be identified based on their clean portrait-like photos, instead of their prior video recordings. This results in three major challenges; large domain discrepancy between violence videos and ID-photos, the lack of video examples for most individuals and limited training data availability. To mimic such scenarios, we formulate a realistic domain-transfer problem, where the goal is to transfer the recognition model trained on clean posed images to the target domain of violent videos, where training videos are available only for a subset of subjects. To this end, we introduce the "WildestFaces" dataset, tailored to study cross-domain recognition under a variety of adverse conditions. We divide the task of transferring a recognition model from the domain of clean images to the violent videos into two sub-problems and tackle them using (i) stacked affine-transforms for classifier-transfer, (ii) attention-driven pooling for temporal-adaptation. We additionally formulate a self-attention based model for domain-transfer. We establish a rigorous evaluation protocol for this "clean-to-violent" recognition task, and present a detailed analysis of the proposed dataset and the methods. Our experiments highlight the unique challenges introduced by the WildestFaces dataset and the advantages of the proposed approach

Turkish Guideline for Diagnosis and Treatment of Allergic Rhinitis (ART)

Object: To prepare a national guideline for Oto-rhinolaryngologist who treat allergic rhinitis patient

Prevalence of Anosmia in 10.157 Pediatric COVID-19 Cases: Multicenter Study from Turkey.

Introduction: COVID-19-related anosmia is a remarkable and disease-specific finding. With this multicenter cohort study, we aimed to determine the prevalence of anosmia in pediatric cases with COVID-19 from Turkey and make an objective assessment with a smell awareness questionnaire. Material and Methods: This multicenter prospective cohort study was conducted with pediatric infection clinics in 37 centers in 19 different cities of Turkey between October 2020 and March 2021. The symptoms of 10.157 COVID-19 cases 10-18 years old were examined. Age, gender, other accompanying symptoms, and clinical severity of the disease of cases with anosmia and ageusia included in the study were recorded. The cases were interviewed for the smell awareness questionnaire at admission and one month after the illness. Results: Anosmia was present in 12.5% (1.266/10.157) of COVID-19 cases 10-18 years of age. The complete records of 1053 patients followed during the study period were analyzed. The most common symptoms accompanying symptoms with anosmia were ageusia in 885 (84%) cases, fatigue in 534 cases (50.7%), and cough in 466 cases (44.3%). Anosmia was recorded as the only symptom in 84 (8%) of the cases. One month later, it was determined that anosmia persisted in 88 (8.4%) cases. In the smell awareness questionnaire, the score at admission was higher than the score one month later (P < 0.001). Discussion: With this study, we have provided the examination of a large case series across Turkey. Anosmia and ageusia are specific symptoms seen in cases of COVID-19. With the detection of these symptoms, it should be aimed to isolate COVID-19 cases in the early period and reduce the spread of the infection. Such studies are important because the course of COVID-19 in children differs from adults and there is limited data on the prevalence of anosmia