Segmentation of lung nodules is critical to computer-aided diagnosis systems for lung cancer diagnosis. In recent times, with the application of deep learning in medical imageprocessing, novel architectures have been...
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While personality traits have been traditionally modeled as behavioral constructs, we novelly posit job hireability as a personality construct. To this end, we examine correlates among personality and hireability meas...
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ISBN:
(纸本)9798400716256
While personality traits have been traditionally modeled as behavioral constructs, we novelly posit job hireability as a personality construct. To this end, we examine correlates among personality and hireability measures on the First Impressions Candidate Screening dataset. Modeling hireability as both a discrete and continuous variable, and the big-five OCEAN personality traits as predictors, we utilize (a) multimodal behavioral cues, and (b) personality trait estimates obtained via these cues for hireability prediction (HP). For each of the text, audio and visual modalities, HP via (b) is found to be more effective than (a). Also, superior results are achieved when hireability is modeled as a continuous rather than a categorical variable. Interestingly, eye and bodily visual cues perform comparably to facial cues for predicting personality and hireability. Explanatory analyses reveal that multimodal behaviors impact personality and hireability impressions: e.g., Conscientiousness impressions are impacted by the use of positive adjectives (verbal behavior) and eye movements (non-verbal behavior), confirming prior observations.
Text-to-image (T2I) ReID has attracted a lot of attention in the recent past. CUHK-PEDES, RSTPReid and ICFG-PEDES are the three available benchmarks to evaluate T2I ReID methods. RSTPReid and ICFG-PEDES comprise of id...
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Although computer Numerical Control (CNC) machines were designed to perform tasks with the least human intervention, operator involvement is mandatory to ensure fault-free operations. Numerous technological solutions ...
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ISBN:
(纸本)9780791887240
Although computer Numerical Control (CNC) machines were designed to perform tasks with the least human intervention, operator involvement is mandatory to ensure fault-free operations. Numerous technological solutions utilizing Artificial Intelligence, sensor fusion, Internet of Things (IoT), machine vision, etc., have been developed for process, component, and machine monitoring to impart smartness and autonomous operating abilities. The primary focus of these solutions is to monitor process faults such as tool wear, chatter, static deflections, and cutting forces to assist the operator in minimizing the consequences. The present work develops a vision-based solution for identifying uncommon process abnormalities like improper coolant flow, chip clogging, and tool breakage during CNC milling. The proposed solution replicates the task of a machine operator in identifying these faults and assists in fault-free operations. The study explores the feasibility of utilizing classical and deep learning-based object detection algorithms while developing these solutions. The classical imageprocessing algorithm is ineffective during dynamic process conditions. The deep learning-based algorithm, with an average precision of about 0.75, showed proficiency in abnormalities detection. A Graphical User Interface (GUI) has been developed and integrated with the CNC milling machine to provide an interactive in-process monitoring tool. It is demonstrated that the proposed solution can reduce dependence on a machine operator while monitoring these faults.
Brain-computer interfaces (BCIs) enable direct communication between the human brain and external devices, interpreting signals like Electroencephalogram (EEG) to translate user intentions into commands. While EEG-bas...
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Converting a grayscale image to a visually plausible and perceptually meaningful color image is an exciting research topic in computervision and graphics. However, predicting the chrominance channels from a grayscale...
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Text-to-image (T2I) ReID has attracted a lot of attention in the recent past. CUHK-PEDES, RSTPReid and ICFG-PEDES are the three available benchmarks to evaluate T2I ReID methods. RSTPReid and ICFG-PEDES comprise of id...
详细信息
ISBN:
(纸本)9798400716256
Text-to-image (T2I) ReID has attracted a lot of attention in the recent past. CUHK-PEDES, RSTPReid and ICFG-PEDES are the three available benchmarks to evaluate T2I ReID methods. RSTPReid and ICFG-PEDES comprise of identities from MSMT17 but due to limited number of unique persons, the diversity is limited. On the other hand, CUHK-PEDES comprises of 13,003 identities but has relatively shorter text description on average. Further, these datasets are captured in a restricted environment with limited number of cameras. In order to further diversify the identities and provide dense captions, we propose a novel dataset. Our dataset comprises of 20,000 unique identities captured in the wild and provides a rich dataset for text-to-image ReID. With a minimum of 26 words for a description, each image is densely captioned. We further synthetically generate images and fine-grained captions using Stable-diffusion and BLIP models trained on our dataset. We perform elaborate experiments using state-of-art text-to-image ReID models and vision-language pre-trained models and present a comprehensive analysis of the dataset. Our experiments also reveal that synthetically generated data leads to a substantial performance improvement in both same dataset as well as cross dataset settings. We will release the code and dataset.
The patterns in biometric data, such as fingerprints, iris, etc., are random and distinct from one individual to another, making them ideal for generating unique identities suitable for many applications. This work pr...
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Earth observation satellites provide us with ample amount of raw data for land cover analysis. However, annotating these data is a cumbersome process, subjected to human error which compel us to shift from supervised ...
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Knowledge Distillation is a transfer learning and compression technique that aims to transfer hidden knowledge from a teacher model to a student model. However, this transfer often leads to poor calibration in the stu...
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