Over the past few years, neural networks have made a huge improvement in object recognition and event analysis. However, due to lack of available data, were not efficiently applied expression In this paper, we tackle problem facial analysis using deep network by generating realistic large scale synthetic labeled dataset. We train 3-dimensional convolutional on generated dataset empirically show how presented method can classify expressions. Our addresses four fundamental issues: (i) that is...

Multimodal dimensional emotion recognition has drawn a great attention from the affective computing community and numerous schemes have been extensively investigated, making significant progress in this area. However, several questions still remain unanswered for most of existing approaches including: (i) how to simultaneously learn compact yet representative features multimodal data, (ii) effectively capture complementary streams, (iii) perform all tasks an end-to-end manner. To address...

Abstract Deep learning has been widely adopted in automatic emotion recognition and lead to significant progress the field. However, due insufficient training data, pre-trained models are limited their generalisation ability, leading poor performance on novel test sets. To mitigate this challenge, transfer performed by fine-tuning pr-etrained domains applied. fine-tuned knowledge may overwrite and/or discard important learnt models. In paper, we address issue proposing a PathNet-based...

On the basis of statistical analysis, studies features distribution typhoons in Spratly Archipelago (the South China Sea) for period 1884–2020 were carried out using satellite information. As a result, it was revealed that 229 observed over water area Islands during this period. Of these, 27 had maximum speed 33 m/s. A clear trend is identified towards an increasing number typhoons, which could be major source regional climate risks future. This probably due to intense development East Asian...

The timing of cell divisions in early embryos during the In-Vitro Fertilization (IVF) process is a key predictor embryo viability. However, observing Time-Lapse Monitoring (TLM) time-consuming and highly depends on experts. In this paper, we propose EmbryosFormer, computational model to automatically detect classify from original time-lapse images. Our proposed network designed as an encoder-decoder deformable transformer with collaborative heads. contracting path predicts per-image labels...

Massive data collected on public roads for autonomous driving has become more popular in many locations the world. More leads to concerns about privacy, including but not limited pedestrian faces and surrounding vehicle license plates, which urges robust solutions detecting anonymizing them realistic road-driving scenarios. Existing datasets both face plate detection are either focused or only parking lots. In this paper, we introduce a challenging dataset domain. The is aggregated from...

This paper presents a systematic evaluation of Amazon Kinesis and Apache Kafka for meeting highly demanding application requirements. Results show that can provide high reliability, performance scalability. Cost trade-offs are presented variety data rates, resource utilization, configurations.

Post-processing is an essential step in detecting and correcting errors OCR-generated texts. In this paper, we present automatic OCR post-processing model which comprises both error detection correction phases for output texts of unconstrained Vietnamese handwriting. We propose a hybrid approach generating scoring candidates non-syllable real-syllable based on the linguistic features as well characteristics outputs. evaluate our proposed benchmark database at line level. The experimental...

Abstract Pose-invariant face recognition refers to the problem of identifying or verifying a person by analyzing images captured from different poses. This is challenging due large variation pose, illumination and facial expression. A promising approach deal with pose fulfill incomplete UV maps extracted in-the-wild faces, then attach completed map fitted 3D mesh finally generate 2D faces arbitrary The synthesized increase for training deep models reduce discrepancy during testing phase. In...

Different types of OCR errors often occur in texts due to the low quality scanned document images or limitations software. In this paper, we propose a novel unsupervised approach for error correction. Correction candidates are generated and explored their neighborhoods using correction character edits controlled by an adapted hill-climbing algorithm. characters extracted from only original ground truth texts, which do not depend on training data. A weighted objective function used score rank...

Deep learning models are associated with various deployment challenges. Inference of such is typically very compute-intensive and memory-intensive. In this paper, we investigate the performance deep for a computer vision application used in automotive manufacturing industry. This has demanding requirements that characteristic Big Data systems, including high volume velocity. The to process large set high-definition images real-time appropriate accuracy using learning-based object detection...

Deep learning has been applied to achieve significant progress in emotion recognition. Despite such substantial progress, existing approaches are still hindered by insufficient training data, and the resulting models do not generalize well under mismatched conditions. To address this challenge, we propose a strategy which jointly transfers knowledge learned from rich datasets source-poor datasets. Our method is also able learn cross-domain features lead improved recognition performance....

Obtaining accurate seismic depth images with reliable amplitudes under complex overburdens like salt requires advanced imaging tools least-squares reverse-time migration (LSRTM) that correct for illumination variations due to incomplete acquisition and overburden velocity complexity. To enable quantitative interpretation (QI) in such settings, physically meaningful also need be available the migrated prestack domain, i.e., as a function of reflection angle or azimuth. Therefore...

OCR post-processing is an important step for improving the quality of output texts. Long short-term memory (LSTM) a deep learning model, which has wide-range applications in many domains like time series prediction, natural language processing and speech recognition. In this paper, we propose error correction model using neural machine translation with bidirectional LSTM networks at syllable level. Vietnamese text dataset evaluation outputted from engine based on attention-based...

We propose Pointer-Augmented Neural Memory (PANM) to help neural networks understand and apply symbol processing new, longer sequences of data. PANM integrates an external memory that uses novel physical addresses pointer manipulation techniques mimic human computer abilities. facilitates assignment, dereference, arithmetic by explicitly using pointers access content. Remarkably, it can learn perform these operations through end-to-end training on sequence data, powering various sequential...

Chronic extra-articular infections of the tibial tunnel are rare, and there only a few cases reported in literature, so diagnosis management these still unclear.

The challenge in constructing artificial social agents is to enable adaptation ability novel agents, and called zero-shot coordination (ZSC). A promising approach train the adaptive by interacting with a diverse pool of collaborators, assuming that greater diversity other seen during training, better generalisation. In this paper, we explore an alternative procedure considering behavioural predictability i.e. whether their actions intentions are predictable, use it select set for training...

Effective decision-making in partially observable environments demands robust memory management. Despite their success supervised learning, current deep-learning models struggle reinforcement learning that are and long-term. They fail to efficiently capture relevant past information, adapt flexibly changing observations, maintain stable updates over long episodes. We theoretically analyze the limitations of existing within a unified framework introduce Stable Hadamard Memory, novel model for...

Forecasting temporal processes such as virus spreading in epidemics often requires more than just observed time-series data, especially at the beginning of a wave when data is limited. Traditional methods employ mechanistic models like SIR family, which make strong assumptions about underlying process, represented small set compact differential equations. Data-driven deep neural networks no and can capture generative process detail, but fail long-term forecasting due to limitations. We...