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Many anatomical structures can be described by surface or volume meshes. Machine learning is a promising tool to extract information from these 3D models. However, high-fidelity meshes often contain hundreds of thousands of vertices, which creates ...

Visual Question and Answering is a fast-evolving field of research where we attempt to answer the question based on an image as a context. In this paper, we try to add support for answering questions that are based on emotions with face detection ...

• Questions based on emotions are difficult to answer with only image as context.
• The focus is on questions based on emotions.
• A contribution to the limited work done on compound emotion classification.
• Facial landmark ...

We consider the problem of forming collectives of agents inherent in application domains aligned with Sustainable Development Goals 4 and 11 (i.e., team formation and ridesharing, respectively). We propose a general solution approach based on a ...

• We propose a forecasting architecture that combines deep autoregressive models with a Spectral Attention (SA) module, merging global and local frequency ...

Time series forecasting is an important problem across many domains, playing a crucial role in multiple real-world applications. In this paper, we propose a forecasting architecture that combines deep autoregressive models with a ...

In humans, Attention is a core property of all perceptual and cognitive operations. Given our limited ability to process competing sources, attention mechanisms select, modulate, and focus on the information most relevant to behavior. For decades, ...

Deep-learning based natural language processing (NLP) models are proven vulnerable to adversarial attacks. However, there is currently insufficient research that studies attacks to neural machine translations (NMTs) and examines the robustness of ...

• Different from conventional works that employ either the hierarchical RNN for structure modeling or the self-attention mechanism for long-range dependencies, ...

In this paper, we propose a multiscale hierarchical attention approach for supervised video summarization. Different from most existing supervised methods which employ bidirectional long short-term memory networks, our method exploits ...

With the proliferation of remote sensing images, how to segment buildings more accurately in remote sensing images is a critical challenge. First, most networks have poor recognition ability on high resolution images, resulting in blurred ...

A Software Engineering Manager (EM) has to cater to the demand for higher reliability and resilience in Production while simultaneously addressing the evolution of software architecture from monolithic applications to multi-cloud distributed ...

Today, there are many publicly available data sources, such as online museum catalogues, Wikipedia, and social media, in the cultural heritage domain. Yet, the data is heterogeneous and complex (diverse, multi-modal, sparse, and noisy). In ...

Nowadays, attention models are one of the popular candidates for speech recognition. So far, many studies mainly focus on the encoder structure or the attention module to enhance the performance of these models. However, mostly ignore the decoder. ...

Historical ciphered manuscripts are a type of document where graphical symbols are used to encrypt their content instead of regular text. Nowadays, expert transcriptions can be found in libraries alongside the corresponding manuscript images. ...

In histopathological image analysis, cell nucleus segmentation plays an important role in the clinical analysis and diagnosis of cancer. However, due to the different morphology of cells, uneven staining and the existence of a large number of ...

Currently, video-based Sign language recognition (SLR) has been extensively studied using deep learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). In addition, using multi view attention ...

• Multi view sign language recognition with deep learning.
• Motion based attention ...

In the context of multiple myeloma, patient diagnosis and treatment planning involve the medical analysis of full-body Positron Emission Tomography (PET) images. There has been a growing interest in linking quantitative measurements extracted from ...

Cryogenic electron microscopy (cryo-EM) has become an enabling technology in drug discovery and in understanding molecular bases of disease by producing near-atomic resolution (less than 0.4 nm) 3D reconstructions of biological macro-molecules. ...

Segmentation and accurate localization of nuclei in histopathological images is a very challenging problem, with most existing approaches adopting a supervised strategy. These methods usually rely on manual annotations that require a lot of time ...

Recent research in convolutional and recurrent neural networks has fueled incredible advances in video understanding. We propose a video captioning framework that achieves the performance and quality necessary to be deployed in distributed ...

• Bag-of-Words models boost their performance when making use of context.
• Context ...

Semantic Textual Similarity and Natural Language Inference are two popular natural language understanding tasks used to benchmark sentence representation models where two sentences are paired. In such tasks sentences are represented as ...

Question-answer matching can be viewed as a puzzle where missing pieces of information are provided by the answer. To solve this puzzle, one must understand the question to find out a correct answer. Semantic-based matching models rely mainly in ...