Iranian Emotional Face Database: Acquisition and Validation of a Stimulus Set of Basic Facial Expressions

Facial expressions play an essential role in social interactions. Databases of face images have furnished theories of emotion perception, as well as having applications in other disciplines such as facial recognition technology. However, the faces of many ethnicities remain largely underrepresented in the existing face databases, which can impact the generalizability of the theories and technologies developed based on them. Here, we present the first survey-validated database of Iranian faces. It consists of 248 images from 40 Iranian individuals portraying six motional expressions – anger, sadness, fear, disgust, happiness, and surprise – as well as the neutral state. The photos were taken in a studio setting, following the common scenarios of emotion induction, and controlling for conditions of lighting, camera set up, and model's head posture. An evaluation survey confirmed a high agreement between the models’ intended expressions and the raters’ perception of them. The database is freely available online for academic research purposes

Iranian Emotional Face Database

Iranian Emotional Face Database is a set of images containing 248 face images displaying seven different facial emotional expressions including neutral, happy, sad, angry, disgusted, fearful and surprised. All images have been taken under the constant situation of lighting and camera set up from 40 individuals (25 male and 15 female in the age between 18-35)

ModelVsBaby: a Developmentally Motivated Benchmark of Out-of-Distribution Object Recognition

Deep neural networks have recently become remarkable computational tools for thinking about human visual learning. Recent studies have explored the effects of altering naturalistic images and compared the responses of both humans and models, providing valuable insights into their functioning and how deep neural networks can shape our understanding of human learning. Critically, much of human visual learning happens throughout early development. Yet, well-controlled benchmarks comparing AI models with young humans are scarce. Here, we present a developmentally motivated benchmark of out-of-distribution (OOD) object recognition. Our benchmark, ModelVsBaby, includes a set of OOD conditions that have long been studied in the vision science literature, and are expected to be sensitive to the development of OOD object-recognition in humans: silhouette, geon, occluded, blurred, crowded background, and a baseline realistic condition. Along with the stimuli, we release a unique dataset of the responses of 2-year-old children to the stimuli. Our preliminary analyses of the dataset show several interesting patterns: 2-year-olds achieve 80% accuracy in the silhouette condition, nearly as well as in the realistic condition (chance=12%). They also perform well above chance, near 60% accuracy, on the other challenging conditions. We also evaluate image-text association (CLIP) models trained on varying amounts of internet-scale datasets. The model performances show that with enough data, all conditions are learnable by artificial learners. However, Realistic and Silhouette are learned with fewer training data similar to humans. Our benchmark stimuli and infant responses, provide an essential steppingstone for building computational models that are aligned with humans both in terms of the learning outcomes as well as the learning trajectory. This endeavor can furnish creating better models of visual development as well as improving the efficiency of AI systems for practical applications. Future work may use the benchmark stimuli to test more age groups, and provide a detailed comparison of models of various flavors in terms of “developmental alignment"

PARSINLU: A Suite of Language Understanding Challenges for Persian

Despite the progress made in recent years in addressing natural language understanding (NLU) challenges, the majority of this progress remains to be concentrated on resource-rich languages like English. This work focuses on Persian language, one of the widely spoken languages in the world, and yet there are few NLU datasets available for this language. The availability of high-quality evaluation datasets is a necessity for reliable assessment of the progress on different NLU tasks and domains. We introduce PARSINLU, the first benchmark in Persian language that includes a range of language understanding tasks-reading comprehension, textual entailment, and so on. These datasets are collected in a multitude of ways, often involving manual annotations by native speakers. This results in over 14.5k new instances across 6 distinct NLU tasks. Additionally, we present the first results on state-of-the-art monolingual and multilingual pre-trained language models on this benchmark and compare them with human performance, which provides valuable insights into our ability to tackle natural language understanding challenges in Persian. We hope PARSINLU fosters further research and advances in Persian language understanding.(1