Multimodal assessment of apparent personality using feature attention and error consistency constraint

Aslan, Süleyman; Güdükbay, Uğur; Dibeklioğlu, Hamdi

doi:10.1016/j.imavis.2021.104163

Cited by 14 publications

(4 citation statements)

References 33 publications

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“…Jia et al [28] construct a spatial and semantic consistency framework, which models the inter-image relation of the same attribute, for pedestrian attribute recognition. Aslan et al [29] develop a novel multimodal method based on three subnetworks (e.g., ResNet, VGGish, and ELMo), for the estimation of apparent personality traits. Moreover, they leverage additional long short-term memory (LSTM) layers to exploit temporal information.…”

Section: A Person Attribute Recognitionmentioning

confidence: 99%

Drop Loss for Person Attribute Recognition With Imbalanced Noisy-Labeled Samples

Yan

Xue

et al. 2023

IEEE Trans. Cybern.

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Person attribute recognition (PAR) aims to simultaneously predict multiple attributes of a person. Existing deep learning-based PAR methods have achieved impressive performance. Unfortunately, these methods usually ignore the fact that different attributes have an imbalance in the number of noisylabeled samples in the PAR training datasets, thus leading to suboptimal performance. To address the above problem of imbalanced noisy-labeled samples, we propose a novel and effective loss called drop loss for PAR. In the drop loss, the attributes are treated differently in an easy-to-hard way. In particular, the noisy-labeled candidates, which are identified according to their gradient norms, are dropped with a higher drop rate for the harder attribute. Such a manner adaptively alleviates the adverse effect of imbalanced noisy-labeled samples on model learning. To illustrate the effectiveness of the proposed loss, we train a simple ResNet-50 model based on the drop loss and term it DropNet. Experimental results on two representative PAR tasks (including facial attribute recognition and pedestrian attribute recognition) demonstrate that the proposed DropNet achieves comparable or better performance in terms of both balanced accuracy and classification accuracy over several state-of-the-art PAR methods.

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Section: A Person Attribute Recognitionmentioning

confidence: 99%

Drop Loss for Person Attribute Recognition With Imbalanced Noisy-Labeled Samples

Yan

Xue

et al. 2023

IEEE Trans. Cybern.

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“…Recent studies in human personality analysis follow multi-modal approaches where models utilize sound and image-based features together. 26 The lack of full-body captures in most human-personality datasets causes difficulties for an in-depth analysis of the motion characteristics. 5 Consequently, motion capture animation datasets are more suitable for movement analysis.…”

Section: Related Workmentioning

confidence: 99%

Personality expression in cartoon animal characters using Sasang typology

Mailee,

Sonlu,

Güdükbay

2023

Computer Animation & Virtual

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The movement style is an adequate descriptor of different personalities. While many studies investigate the relationship between apparent personality and high‐level motion qualities in humans, similar research for animal characters still needs to be done. The variety in animals' skeletal configurations and texture complicates their pose estimation process. Our affect analysis framework includes a workflow for pose extraction in animal characters and a parameterization of the high‐level animal motion descriptors inspired by Laban movement analysis. Using a data set of quadruped walk cycles, we prove the display of typologies in cartoon animal characters, reporting the point‐biserial correlation between our motion parameters and the Sasang categories that reflect different personalities.

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“…A multimodal approach for perceiving personality traits was proposed by employing well-known deep structures (ResNet-v2-101 and VGGish) [37]. The LSTM network for using temporal information was added at the end.…”

Section: Automatic Personality Perceptionmentioning

confidence: 99%

Hyper-Parameter Optimization of Stacked Asymmetric Auto-Encoders for Automatic Personality Traits Perception

Zaferani

Teshnehlab

Khodadadian

et al. 2022

Sensors

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In this work, a method for automatic hyper-parameter tuning of the stacked asymmetric auto-encoder is proposed. In previous work, the deep learning ability to extract personality perception from speech was shown, but hyper-parameter tuning was attained by trial-and-error, which is time-consuming and requires machine learning knowledge. Therefore, obtaining hyper-parameter values is challenging and places limits on deep learning usage. To address this challenge, researchers have applied optimization methods. Although there were successes, the search space is very large due to the large number of deep learning hyper-parameters, which increases the probability of getting stuck in local optima. Researchers have also focused on improving global optimization methods. In this regard, we suggest a novel global optimization method based on the cultural algorithm, multi-island and the concept of parallelism to search this large space smartly. At first, we evaluated our method on three well-known optimization benchmarks and compared the results with recently published papers. Results indicate that the convergence of the proposed method speeds up due to the ability to escape from local optima, and the precision of the results improves dramatically. Afterward, we applied our method to optimize five hyper-parameters of an asymmetric auto-encoder for automatic personality perception. Since inappropriate hyper-parameters lead the network to over-fitting and under-fitting, we used a novel cost function to prevent over-fitting and under-fitting. As observed, the unweighted average recall (accuracy) was improved by 6.52% (9.54%) compared to our previous work and had remarkable outcomes compared to other published personality perception works.

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Multimodal assessment of apparent personality using feature attention and error consistency constraint

Cited by 14 publications

References 33 publications

Drop Loss for Person Attribute Recognition With Imbalanced Noisy-Labeled Samples

Drop Loss for Person Attribute Recognition With Imbalanced Noisy-Labeled Samples

Personality expression in cartoon animal characters using Sasang typology

Hyper-Parameter Optimization of Stacked Asymmetric Auto-Encoders for Automatic Personality Traits Perception

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