A fusion model for multi-label emotion classification based on BERT and topic clustering

2020 ◽  
Author(s):  
Fei Ding ◽  
Xin Kang ◽  
Shun Nishide ◽  
Zhijin Guan ◽  
Fuji Ren
Keyword(s):  
Emotion Classification ◽  
Fusion Model ◽  
Topic Clustering

An Accelerated Cue Combination Principle Accounts for Multi-cue Depth Perception

2020 ◽  
Vol 3 (1) ◽  
pp. 10501-1-10501-9
Author(s):  
Christopher W. Tyler
Keyword(s):  
Belief Propagation ◽  
Probability Distributions ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Fusion Model ◽  
Cue Combination ◽  
Depth Estimate ◽  
Appropriate Behavior ◽  
Depth Cues ◽  
Bayesian Averaging

Abstract For the visual world in which we operate, the core issue is to conceptualize how its three-dimensional structure is encoded through the neural computation of multiple depth cues and their integration to a unitary depth structure. One approach to this issue is the full Bayesian model of scene understanding, but this is shown to require selection from the implausibly large number of possible scenes. An alternative approach is to propagate the implied depth structure solution for the scene through the “belief propagation” algorithm on general probability distributions. However, a more efficient model of local slant propagation is developed as an alternative.The overall depth percept must be derived from the combination of all available depth cues, but a simple linear summation rule across, say, a dozen different depth cues, would massively overestimate the perceived depth in the scene in cases where each cue alone provides a close-to-veridical depth estimate. On the other hand, a Bayesian averaging or “modified weak fusion” model for depth cue combination does not provide for the observed enhancement of perceived depth from weak depth cues. Thus, the current models do not account for the empirical properties of perceived depth from multiple depth cues.The present analysis shows that these problems can be addressed by an asymptotic, or hyperbolic Minkowski, approach to cue combination. With appropriate parameters, this first-order rule gives strong summation for a few depth cues, but the effect of an increasing number of cues beyond that remains too weak to account for the available degree of perceived depth magnitude. Finally, an accelerated asymptotic rule is proposed to match the empirical strength of perceived depth as measured, with appropriate behavior for any number of depth cues.

Survey Paper on Gender and Emotion Classification using Facial Expression Detection

2020 ◽  
Author(s):  
Aishwarya Gupta ◽  
Devashish Sharma ◽  
Shaurya Sharma ◽  
Anushree Agarwal
Keyword(s):  
Facial Expression ◽  
Emotion Classification ◽  
Survey Paper ◽  
Gender And Emotion

scholarly journals Spatiotemporal Fusion of Formosat-2 and Landsat-8 Satellite Images: A Comparison of “Super Resolution-Then-Blend” and “Blend-Then-Super Resolution” Approaches

2021 ◽  
Vol 13 (4) ◽  
pp. 606
Author(s):  
Tee-Ann Teo ◽  
Yu-Ju Fu
Keyword(s):  
Satellite Images ◽  
Traditional Approach ◽  
Spectral Band ◽  
Super Resolution ◽  
High Temporal Resolution ◽  
Landsat 8 ◽  
Fusion Model ◽  
Image Blending ◽  
Fine Resolution

The spatiotemporal fusion technique has the advantages of generating time-series images with high-spatial and high-temporal resolution from coarse-resolution to fine-resolution images. A hybrid fusion method that integrates image blending (i.e., spatial and temporal adaptive reflectance fusion model, STARFM) and super-resolution (i.e., very deep super resolution, VDSR) techniques for the spatiotemporal fusion of 8 m Formosat-2 and 30 m Landsat-8 satellite images is proposed. Two different fusion approaches, namely Blend-then-Super-Resolution and Super-Resolution (SR)-then-Blend, were developed to improve the results of spatiotemporal fusion. The SR-then-Blend approach performs SR before image blending. The SR refines the image resampling stage on generating the same pixel-size of coarse- and fine-resolution images. The Blend-then-SR approach is aimed at refining the spatial details after image blending. Several quality indices were used to analyze the quality of the different fusion approaches. Experimental results showed that the performance of the hybrid method is slightly better than the traditional approach. Images obtained using SR-then-Blend are more similar to the real observed images compared with images acquired using Blend-then-SR. The overall mean bias of SR-then-Blend was 4% lower than Blend-then-SR, and nearly 3% improvement for overall standard deviation in SR-B. The VDSR technique reduces the systematic deviation in spectral band between Formosat-2 and Landsat-8 satellite images. The integration of STARFM and the VDSR model is useful for improving the quality of spatiotemporal fusion.

scholarly journals Improving the Resolution and Accuracy of Groundwater Level Anomalies Using the Machine Learning-Based Fusion Model in the North China Plain

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 46
Author(s):  
Gangqiang Zhang ◽  
Wei Zheng ◽  
Wenjie Yin ◽  
Weiwei Lei
Keyword(s):  
Machine Learning ◽  
Data Fusion ◽  
Groundwater Level ◽  
North China Plain ◽  
North China ◽  
Gradient Boosting ◽  
Fusion Model ◽  
The North ◽  
The North China Plain ◽  
Prediction Module

The launch of GRACE satellites has provided a new avenue for studying the terrestrial water storage anomalies (TWSA) with unprecedented accuracy. However, the coarse spatial resolution greatly limits its application in hydrology researches on local scales. To overcome this limitation, this study develops a machine learning-based fusion model to obtain high-resolution (0.25°) groundwater level anomalies (GWLA) by integrating GRACE observations in the North China Plain. Specifically, the fusion model consists of three modules, namely the downscaling module, the data fusion module, and the prediction module, respectively. In terms of the downscaling module, the GRACE-Noah model outperforms traditional data-driven models (multiple linear regression and gradient boosting decision tree (GBDT)) with the correlation coefficient (CC) values from 0.24 to 0.78. With respect to the data fusion module, the groundwater level from 12 monitoring wells is incorporated with climate variables (precipitation, runoff, and evapotranspiration) using the GBDT algorithm, achieving satisfactory performance (mean values: CC: 0.97, RMSE: 1.10 m, and MAE: 0.87 m). By merging the downscaled TWSA and fused groundwater level based on the GBDT algorithm, the prediction module can predict the water level in specified pixels. The predicted groundwater level is validated against 6 in-situ groundwater level data sets in the study area. Compare to the downscaling module, there is a significant improvement in terms of CC metrics, on average, from 0.43 to 0.71. This study provides a feasible and accurate fusion model for downscaling GRACE observations and predicting groundwater level with improved accuracy.

scholarly journals Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Jing Yan ◽  
Bin Zhang ◽  
Shuaitong Zhang ◽  
Jingliang Cheng ◽  
Xianzhi Liu ◽  
...  
Keyword(s):  
Image Fusion ◽  
Molecular Subtypes ◽  
Progression Free Survival ◽  
Quantitative Mri ◽  
Promoter Mutation ◽  
Fusion Model ◽  
Free Survival ◽  
Contrast Enhanced ◽  
Apparent Diffusion ◽  
The Status

AbstractGliomas can be classified into five molecular groups based on the status of IDH mutation, 1p/19q codeletion, and TERT promoter mutation, whereas they need to be obtained by biopsy or surgery. Thus, we aimed to use MRI-based radiomics to noninvasively predict the molecular groups and assess their prognostic value. We retrospectively identified 357 patients with gliomas and extracted radiomic features from their preoperative MRI images. Single-layered radiomic signatures were generated using a single MR sequence using Bayesian-regularization neural networks. Image fusion models were built by combing the significant radiomic signatures. By separately predicting the molecular markers, the predictive molecular groups were obtained. Prognostic nomograms were developed based on the predictive molecular groups and clinicopathologic data to predict progression-free survival (PFS) and overall survival (OS). The results showed that the image fusion model incorporating radiomic signatures from contrast-enhanced T1-weighted imaging (cT1WI) and apparent diffusion coefficient (ADC) achieved an AUC of 0.884 and 0.669 for predicting IDH and TERT status, respectively. cT1WI-based radiomic signature alone yielded favorable performance in predicting 1p/19q status (AUC = 0.815). The predictive molecular groups were comparable to actual ones in predicting PFS (C-index: 0.709 vs. 0.722, P = 0.241) and OS (C-index: 0.703 vs. 0.751, P = 0.359). Subgroup analyses by grades showed similar findings. The prognostic nomograms based on grades and the predictive molecular groups yielded a C-index of 0.736 and 0.735 in predicting PFS and OS, respectively. Accordingly, MRI-based radiomics may be useful for noninvasively detecting molecular groups and predicting survival in gliomas regardless of grades.

Deep Multi-Stage Approach For Emotional Body Gesture Recognition In Job Interview

2021 ◽  
Author(s):  
Intissar Khalifa ◽  
Ridha Ejbali ◽  
Raimondo Schettini ◽  
Mourad Zaied
Keyword(s):  
Gesture Recognition ◽  
Affective Computing ◽  
Psychological State ◽  
Sources Of Information ◽  
Emotion Classification ◽  
Convolutional Network ◽  
Job Interview ◽  
Multi Stage ◽  
Representation Technique ◽  
The Subject

Abstract Affective computing is a key research topic in artificial intelligence which is applied to psychology and machines. It consists of the estimation and measurement of human emotions. A person’s body language is one of the most significant sources of information during job interview, and it reflects a deep psychological state that is often missing from other data sources. In our work, we combine two tasks of pose estimation and emotion classification for emotional body gesture recognition to propose a deep multi-stage architecture that is able to deal with both tasks. Our deep pose decoding method detects and tracks the candidate’s skeleton in a video using a combination of depthwise convolutional network and detection-based method for 2D pose reconstruction. Moreover, we propose a representation technique based on the superposition of skeletons to generate for each video sequence a single image synthesizing the different poses of the subject. We call this image: ‘history pose image’, and it is used as input to the convolutional neural network model based on the Visual Geometry Group architecture. We demonstrate the effectiveness of our method in comparison with other methods in the state of the art on the standard Common Object in Context keypoint dataset and Face and Body gesture video database.

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