Learning Dynamic GMM for Attention Distribution on Single-Face Videos

The goal of this special issue was to bring together experts working on applying models, principles, and knowledge of human audio-visual perception and cognition to optimize video processing algorithms and applications. This is an area where our deeper understanding of visual perception to video processing will lead to new breakthroughs in visual information processing. Attentional focus is an important aspect of perception that helps understand user interest and intent. Determining points of saliency is computationally complex. Compressed-domain methods thus become valuable tools for developing computationally efficient and practical solutions. In BCompressed-Domain Correlates of Human Fixations in Dynamic Scenes^ (10. 1007/s11042-015-2802-3) authors present a method for detecting points of fixation in H.264/ AVC video using motion vectors, block coding modes and coded residuals parsed from a H. 164/AVC bitstream. Egocentric videos are captured usingwearable cameras and used to detect a wearer’s point of view. Amount of video captured using wearable cameras is increasing and saliency detection in such videos enables applications such as efficient video summarization. In BGeometrical Cues in Visual Saliency Models for Active Object Recognition in Egocentric Videos^ (10.1007/ s11042-015-2803-2) authors use geometrical cues to improve saliency detection in videos. Action recognition from videos is a challenging task with many applications including surveillance and social behavior understanding. Inspired by models of neural response to visual input, the paper entitled BDeep Learning Human Actions from Video via Sparse Filtering and Locally Competitive Algorithms^ (10.1007/s11042-015-2808-x) presents a method that combines sparse filtering with locally competitive algorithms for applications in action recognition. Multimed Tools Appl (2015) 74:10053–10056 DOI 10.1007/s11042-015-2957-y

Saliency detection for images and videos becomes increasingly popular due to its wide applicability. Enormous research efforts have been focused on saliency detection, but it still has some issues in maintaining spatiotemporal consistency of videos and uniformly highlighting entire objects. To address these issues, this paper proposes a superpixel-level spatiotemporal saliency model for saliency detection in videos. To detect salient object, we extract multiple spatiotemporal features combined with intra-consistency motion information preliminarily. Meanwhile, considering inter-consistency of foreground in videos, a set of foreground locations are obtained from previous frames. Then, we introduce foreground-background and local foreground contrast saliency cues of those features using the location prior information of foreground. These two improved contrast saliency cues uniformly highlight the entire object and suppress the background effectively. Finally, we use an interactively dynamic fusion method to integrate the output spatial and temporal saliency maps. The proposed approach is validated on challenging sets of video sequences. Subjective observations and objective evaluations demonstrate that the proposed model achieves a better performance on saliency detection compared with the state-of-the-art spatiotemporal saliency methods.

Spatiotemporal saliency detection based on superpixel-level trajectory

Salient object detection in video using deep non-local neural networks

Speech Emotion Recognition (SER) plays a vital role in various real-world applications, from mental health diagnostics to human-computer interaction. This research examines the integration of Stacked Long Short-Term Memory (LSTM) networks and Hidden Markov Models (HMM) for SER, addressing the limitations of traditional models like Gaussian Mixture Models (GMM) and Support Vector Machines (SVM). While GMMs and SVMs offer decent performance, their inability to model temporal dynamics limits their accuracy in detecting complex emotions. The proposed hybrid model leverages LSTM’s strength in handling sequential data and HMM’s ability to model emotional transitions, making it highly suitable for real-world noisy environments. Preprocessing techniques such as MFCC and LPCC are applied to enhance feature extraction, and benchmark datasets like IEMOCAP and RAVDESS are used for evaluation. Finally, The Systematic review highlights the superior role of the hybrid model's performance on SER and sets the stage for a significant shift in future research in addressing bias and fairness in SER systems by combining LSTM and HMM as hybrid model. In recent studies, context-aware emotion recognition models are commonly evaluated using existing datasets such as IEMOCAP, MELD, DailyDialog, and SEMAINE, which provide conversational and multimodal emotional data. Features used often include acoustic features as MFCCs, spectrograms, prosodic features, linguistic features as BERT or transformer embeddings, and visual cues such as facial landmarks or video frames. Many models also incorporate contextual embeddings from transformers, graph attention networks, or sequential memory modules to capture speaker history and emotional shifts.

Since it is one of the world's most significant financial markets, the foreign exchange (Forex) market has attracted a large number of investors. Accurately anticipating the forex trend has remained a popular but difficult issue to aid Forex traders' trading decisions. It is always a question of how precise a Forex prediction can be because of the market's tremendous complexity. The fast advancement of machine learning in recent decades has allowed artificial neural networks to be effectively adapted to several areas, including the Forex market. As a result, a slew of research articles aimed at improving the accuracy of currency forecasting has been released. The Long Short-Term Memory (LSTM) neural network, which is a special kind of artificial neural network developed exclusively for time series data analysis, is frequently used. Due to its high learning capacity, the LSTM neural network is increasingly being utilized to predict advanced Forex trading based on previous data. This model, on the other hand, can be improved by stacking it. The goal of this study is to choose a dataset using the Hurst exponent, then use a two-layer stacked Long Short-Term Memory (TLS-LSTM) neural network to forecast the trend and conduct a correlation analysis. The Hurst exponent (h) was used to determine the predictability of the Australian Dollar and United States Dollar (AUD/USD) dataset. TLS-LSTM algorithm is presented to improve the accuracy of Forex trend prediction of Australian Dollar and United States Dollar (AUD/USD). A correlation study was performed between the AUD/USD, the Euro and the Australian Dollar (EUR/AUD), and the Australian Dollar and the Japanese Yen (AUD/JPY) to see how AUD/USD movement affects EUR/AUD and AUD/JPY. The model was compared with Single-Layer Long Short-Term (SL-LSTM), Multilayer Perceptron (MLP), and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Improved Firefly Algorithm Long Short-Term Memory. Based on the evaluation metrics Mean Square Error (MSE), Root Mean Square Error, and Mean Absolute Error, the suggested TLS-LSTM, whose data selection is based on the Hurst exponent (h) value of 0.6026, outperforms SL-LSTM, MLP, and CEEMDAN-IFALSTM. The correlation analysis conducted shows both positive and negative relations between AUD/USD, EUR/AUD, and AUD/JPY which means that a change in AUD/USD will affect EUR/AUD and AUD/JPY as recorded depending on the magnitude of the correlation coefficient (r).

Due to complex lighting circumstances and other factors, visual saliency recognition of underwater images is not as good as visual saliency detection models that function well on land when applied to underwater environments. The dearth of large-scale annotated underwater picture datasets has further hindered the development of visual saliency detection in underwater images. we propose an attention-based approach for underwater image saliency detection to address this issue. The backbone network is pre-trained using both land and underwater image datasets, which effectively solves the problem of fewer datasets. After that, a residual refinement module incorporating a channel-space attention mechanism is merged, the model as a whole is trained end-to-end using underwater image datasets, and the model accuracy is experimentally tested. The experimental results in three underwater image datasets show that our method is better than several other methods in the mainstream evaluation indicators of the saliency detection.

Language models based on Long Short-Term Memory (LSTM) neural networks have been widely applied to automatic speech recognition, natural language processing research. However, current LSTMs are trained by employing the cross-entropy loss function, which only considers the target category without considering the competing categories during the training processes. Thus, current training methods cannot fully exploit the discriminative information provided by the data labels. To tackle this problem, we propose a Large Margin Long Short-Term Memory Neural Network (LMLSTM) model in this paper. Our model employs the large margin discriminative principle as a heuristic term to navigate the convergence process during training. Our model has improved the discriminative ability of the original LSTM while maintaining the capability when generating sequential data. Our proposed large margin term was tested on the Penn Treebank corpus language modelling task. Experimental results demonstrate that the proposed LMLSTM model outperforms current LSTM models in terms of accuracy and perplexity without increasing the depth.

Saliency mechanism has been considered crucial in the human visual system and helpful to object detection and recognition. This paper addresses an information theoretic model for visual saliency detection. It consists of two steps: first, using the Non-negative Matrix Factorization with sparseness constraints (NMFsc) algorithm to learn the basis functions from a set of randomly sampled natural image patches; and then, applying information theoretic principle to generate the saliency map by the Salient Information (SI) which is calculated from the coefficients represented by basis functions. We compare our model with the previous methods on natural images. Experimental results show that our model performs better than existing approaches.

Economy and carbon emissions optimization of different countries or areas in the world using an improved Attention mechanism based long short term memory neural network

In the realm of water resource management, meticulous monitoring and control methodologies are quintessential to the refinement of wastewater treatment processes. This research elucidates an avant-garde methodology for forecasting the Chemical Oxygen Demand (COD), an instrumental indicator of water quality, by harnessing the capabilities of long short-term memory (LSTM) neural networks in conjunction with Internet of Things (IoT) paradigms. The efficacy of the LSTM model is juxtaposed with that of an advanced Deep Belief Network (DBN), as well as contemporary models like a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) hybrid model and a Transformer-based model, employing data sourced from a wastewater treatment facility located in Changsha. The empirical findings show that notwithstanding the comparable training durations used, the LSTM model exhibits a preeminent error rate of merely 7%, thus surpassing the DBN model (which has an error rate of 35%), the CNN-LSTM model (registering a 22% error rate), and the Transformer-based model (with a 17% error rate) in its predictive precision. This research underscores the potential of integrating an astute wastewater control system with IoT and LSTM models, thereby hinting at prospective enhancements in the sustainability and operational efficacy of wastewater treatment installations.

This study constructs a model suitable for studying the performance of a Japanese speech recognition translation system based on a long short-term memory (LSTM) neural network. The impact of the matrix on the accuracy and recognition ratio of the speech recognition system is considered. This study provides a general introduction to the LSTM neural network, gives an overview of a Japanese speech recognition translation system, and presents experimental data to evaluate its performance. Then, the LSTM neural network is recommended for studying the Japanese speech translation recognition system, and a suitable model is established to conduct experiments on the system. By comparing example analyses, the experimental results show that the optimized PLSTM (Part-Aware Long Short-Term Memory) achieved an 8.09% improvement over an unoptimized LSTM network while maintaining recognition accuracy.

Spherical images or videos, as typical non-euclidean data, are usually stored in the form of 2D panoramas obtained through an equirectangular projection, which is neither equal area nor conformal. The distortion caused by the projection limits the performance of vanilla Deep Neural Networks (DNNs) designed for traditional euclidean data. In this paper, we design a novel Spherical Deep Neural Network (DNN) to deal with the distortion caused by the equirectangular projection. Specifically, we customize a set of components, including a spherical convolution, a spherical pooling, a spherical ConvLSTM cell and a spherical MSE loss, as the replacements of their counterparts in vanilla DNNs for spherical data. The core idea is to change the identical behavior of the conventional operations in vanilla DNNs across different feature patches so that they will be adjusted to the distortion caused by the variance of sampling rate among different feature patches. We demonstrate the effectiveness of our Spherical DNNs for saliency detection and gaze estimation in 360° videos. For saliency detection, we take the temporal coherence of an observer's viewing process into consideration and propose to use a Spherical U-Net and a Spherical ConvLSTM to predict the saliency maps for each frame sequentially. As for gaze prediction, we propose to leverage a Spherical Encoder Module to extract spatial panoramic features, then we combine them with the gaze trajectory feature extracted by an LSTM for future gaze prediction. To facilitate the study of the 360° videos saliency detection, we further construct a large-scale 360° video saliency detection dataset that consists of 104 360° videos viewed by 20+ human subjects. Comprehensive experiments validate the effectiveness of our proposed Spherical DNNs for 360 ° handwritten digit classification and sport classification, saliency detection and gaze tracking in 360° videos. We also visualize the regions contributing to the classification decisions in our proposed Spherical DNNs via the Grad-CAM technique in the classification task, and the results show that our Spherical DNNs constantly leverage reasonable and important regions for decision making, regardless the large distortions. All codes and dataset are available on https://github.com/svip-lab/SphericalDNNs.

Video saliency detection aims to pop out the most salient regions in every frame of a video. Up to now, many efforts have been made from various aspects for video saliency detection. Unfortunately, the existing video saliency models are very likely to fail in challenging videos with complicated motions and complex scenes. Therefore, in this paper, we propose a novel framework to improve the saliency detection results generated by existing video saliency models. The proposed framework consists of three key steps including localized estimation, spatiotemporal refinement, and saliency update. Specifically, the initial saliency map of each frame in a video is first generated by using an existing saliency model. Then, by considering the temporal consistency and strong correlation among adjacent frames, the localized estimation models, which are generated by training the random forest regressor within a local temporal window, are employed to generate the temporary saliency map. Finally, by taking the appearance and motion information of salient objects into consideration, the spatiotemporal refinement step is deployed to further improve the temporary saliency map and generate the final saliency map. Furthermore, such an improved saliency map is then utilized to update the initial saliency map and provide reliable cues for saliency detection in the next frame. The experimental results on four challenging datasets demonstrate that the proposed framework is able to consistently and significantly improve the saliency detection performance of various video saliency models, thereby achieving the state-of-the-art performance.

Saliency detection is an active topic in the multimedia field. Most previous works on saliency detection focus on 2D images. However, these methods are not robust against complex scenes which contain multiple objects or complex backgrounds. Recently, depth information supplies a powerful cue for saliency detection. In this paper, we propose a multilayer backpropagation saliency detection algorithm based on depth mining by which we exploit depth cue from three different layers of images. The proposed algorithm shows a good performance and maintains the robustness in complex situations. Experiments' results show that the proposed framework is superior to other existing saliency approaches. Besides, we give two innovative applications by this algorithm, such as scene reconstruction from multiple images and small target object detection in video.