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Found 54 papers, 5 papers with code
Towards Deeper and Better Multi-view Feature Fusion for 3D Semantic Segmentation
On the one hand, the unidirectional projection enforces our model focused more on the core task, i. e., 3D segmentation; on the other hand, unlocking the bidirectional to unidirectional projection enables a deeper cross-domain semantic alignment and enjoys the flexibility to fuse better and complicated features from very different spaces.
Audio-Visual Speech Enhancement and Separation by Leveraging Multi-Modal Self-Supervised Embeddings
In summary, our results confirm the effectiveness of our proposed model for the AVSS task with proper fine-tuning strategies, demonstrating that multi-modal self-supervised embeddings obtained from AV-HUBERT can be generalized to audio-visual regression tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Unlocking the potential of two-point cells for energy-efficient and resilient training of deep nets
Context-sensitive two-point layer 5 pyramidal cells (L5PCs) were discovered as long ago as 1999.
A Novel Frame Structure for Cloud-Based Audio-Visual Speech Enhancement in Multimodal Hearing-aids
In this paper, we design a first of its kind transceiver (PHY layer) prototype for cloud-based audio-visual (AV) speech enhancement (SE) complying with high data rate and low latency requirements of future multimodal hearing assistive technology.
WikiDes: A Wikipedia-Based Dataset for Generating Short Descriptions from Paragraphs
In this paper, we introduce WikiDes, a novel dataset to generate short descriptions of Wikipedia articles for the problem of text summarization.
Canonical Cortical Graph Neural Networks and its Application for Speech Enhancement in Audio-Visual Hearing Aids
Despite the recent success of machine learning algorithms, most models face drawbacks when considering more complex tasks requiring interaction between different sources, such as multimodal input data and logical time sequences.
Towards Simple and Accurate Human Pose Estimation with Stair Network
In this paper, we focus on tackling the precise keypoint coordinates regression task.
A Novel Speech Intelligibility Enhancement Model based on CanonicalCorrelation and Deep Learning
Current deep learning (DL) based approaches to speech intelligibility enhancement in noisy environments are often trained to minimise the feature distance between noise-free speech and enhanced speech signals.
Multimodal Audio-Visual Information Fusion using Canonical-Correlated Graph Neural Network for Energy-Efficient Speech Enhancement
Our proposed AV CCA-GNN model deals with multimodal representation learning context.
Design of Flexible Meander Line Antenna for Healthcare for Wireless Medical Body Area Networks
The MMA design shows a bandwidth of up to 1282. 4 (450. 5) MHz and provides gains of 3. 03 (4. 85) dBi in the lower and upper operating bands, respectively, showing omnidirectional radiation patterns in free space.
A Speech Intelligibility Enhancement Model based on Canonical Correlation and Deep Learning for Hearing-Assistive Technologies
Current deep learning (DL) based approaches to speech intelligibility enhancement in noisy environments are generally trained to minimise the distance between clean and enhanced speech features.
A Novel Temporal Attentive-Pooling based Convolutional Recurrent Architecture for Acoustic Signal Enhancement
To address this problem, we propose to integrate a novel temporal attentive-pooling (TAP) mechanism into a conventional convolutional recurrent neural network, termed as TAP-CRNN.
Towards Robust Real-time Audio-Visual Speech Enhancement
The human brain contextually exploits heterogeneous sensory information to efficiently perform cognitive tasks including vision and hearing.
Towards Intelligibility-Oriented Audio-Visual Speech Enhancement
To the best of our knowledge, this is the first work that exploits the integration of AV modalities with an I-O based loss function for SE.
PointNu-Net: Simultaneous Multi-tissue Histology Nuclei Segmentation and Classification in the Clinical Wild
Automatic nuclei segmentation and classification plays a vital role in digital pathology.
Ranked #4 on
Multi-tissue Nucleus Segmentation
on CoNSeP
Cloud based Scalable Object Recognition from Video Streams using Orientation Fusion and Convolutional Neural Networks
Herein we demonstrate how a feature-fusion strategy of the orientation components leads to further improving visual recognition accuracy to 97\%.
FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning
Furthermore, to better evaluate the gains, we propose a new dyadic group definition to measure the bias of a link prediction task when paired with group-based fairness metrics.
FastAdaBelief: Improving Convergence Rate for Belief-based Adaptive Optimizers by Exploiting Strong Convexity
%on how to exploit strong convexity to further improve the convergence rate of AdaBelief.
A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling
In order to make this class of functional link adaptive filters (FLAFs) efficient, we propose low-complexity expansions and frequency-domain adaptation of the parameters.
Learning Polar Encodings for Arbitrary-Oriented Ship Detection in SAR Images
In this paper, in order to solve the boundary discontinuity problem in OBB regression, we propose to detect SAR ships by learning polar encodings.
A novel multimodal fusion network based on a joint coding model for lane line segmentation
There has recently been growing interest in utilizing multimodal sensors to achieve robust lane line segmentation.
Conceptual Text Region Network: Cognition-Inspired Accurate Scene Text Detection
The framework utilizes Conceptual Text Regions (CTRs), which is a class of cognition-based tools inheriting good mathematical properties, allowing for sophisticated label design.
A Novel Context-Aware Multimodal Framework for Persian Sentiment Analysis
Most recent works on sentiment analysis have exploited the text modality.
Persuasive Dialogue Understanding: the Baselines and Negative Results
Persuasion aims at forming one's opinion and action via a series of persuasive messages containing persuader's strategies.
Discriminative Dictionary Design for Action Classification in Still Images and Videos
Specifically, we pose the selection of potent local descriptors as filtering based feature selection problem which ranks the local features per category based on a novel measure of distinctiveness.
Improving Aspect-Level Sentiment Analysis with Aspect Extraction
Aspect-based sentiment analysis (ABSA), a popular research area in NLP has two distinct parts -- aspect extraction (AE) and labeling the aspects with sentiment polarity (ALSA).
Deep Learning in Mining Biological Data
Highlighting the role of DL in recognizing patterns in biological data, this article provides - applications of DL to biological sequences, images, and signals data; overview of open access sources of these data; description of open source DL tools applicable on these data; and comparison of these tools from qualitative and quantitative perspectives.
Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations
Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper.
AV Speech Enhancement Challenge using a Real Noisy Corpus
This paper presents, a first of its kind, audio-visual (AV) speech enhacement challenge in real-noisy settings.
A Hybrid Persian Sentiment Analysis Framework: Integrating Dependency Grammar Based Rules and Deep Neural Networks
When no pattern is triggered, the framework switches to its subsymbolic counterpart and leverages deep neural networks (DNN) to perform the classification.
CochleaNet: A Robust Language-independent Audio-Visual Model for Speech Enhancement
In addition, our work challenges a popular belief that a scarcity of multi-language large vocabulary AV corpus and wide variety of noises is a major bottleneck to build a robust language, speaker and noise independent SE systems.
Attributes Guided Feature Learning for Vehicle Re-identification
In particular, our network is end-to-end trained and contains three subnetworks of deep features embedded by the corresponding attributes (i. e., camera view, vehicle type and vehicle color).
LEARNING ADVERSARIAL EXAMPLES WITH RIEMANNIAN GEOMETRY
In this paper, we propose a generalized framework that addresses the learning problem of adversarial examples with Riemannian geometry.
Arabizi sentiment analysis based on transliteration and automatic corpus annotation
Afterwards, we automatically classify the sentiment of the transliterated corpus using an automatically annotated corpus.
Contextual Audio-Visual Switching For Speech Enhancement in Real-World Environments
In this paper, we introduce a novel contextual AV switching component that contextually exploits AV cues with respect to different operating conditions to estimate clean audio, without requiring any SNR estimation.
Saliency Detection via Bidirectional Absorbing Markov Chain
Simultaneously, foreground prior as the virtual absorbing nodes is used to calculate the absorption time and obtain the background possibility.
Exploiting Deep Learning for Persian Sentiment Analysis
The rise of social media is enabling people to freely express their opinions about products and services.
SentiALG: Automated Corpus Annotation for Algerian Sentiment Analysis
In this paper, we present a novel approach to automatically construct an annotated sentiment corpus for Algerian dialect (a Maghrebi Arabic dialect).
Lip-Reading Driven Deep Learning Approach for Speech Enhancement
The proposed audio-visual (AV) speech enhancement framework operates at two levels.
DNN driven Speaker Independent Audio-Visual Mask Estimation for Speech Separation
The process of selective attention in the brain is known to contextually exploit the available audio and visual cues to better focus on target speaker while filtering out other noises.
An Enhanced Binary Particle-Swarm Optimization (E-BPSO) Algorithm for Service Placement in Hybrid Cloud Platforms
Nowadays, hybrid cloud platforms stand as an attractive solution for organizations intending to implement combined private and public cloud applications, in order to meet their profitability requirements.
Multimodal Sentiment Analysis: Addressing Key Issues and Setting up the Baselines
We compile baselines, along with dataset split, for multimodal sentiment analysis.
Complex-valued Neural Networks with Non-parametric Activation Functions
Complex-valued neural networks (CVNNs) are a powerful modeling tool for domains where data can be naturally interpreted in terms of complex numbers.
A Brain-Inspired Trust Management Model to Assure Security in a Cloud based IoT Framework for Neuroscience Applications
To ensure secure and reliable data communication between end-to-end (E2E) devices supported by current IoT and cloud infrastructure, trust management is needed at the IoT and user ends.
Applications of Deep Learning and Reinforcement Learning to Biological Data
Rapid advances of hardware-based technologies during the past decades have opened up new possibilities for Life scientists to gather multimodal data in various application domains (e. g., Omics, Bioimaging, Medical Imaging, and [Brain/Body]-Machine Interfaces), thus generating novel opportunities for development of dedicated data intensive machine learning techniques.
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges
We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications for various learning tasks in the context of networking.
A novel agent-based simulation framework for sensing in complex adaptive environments
In this paper we present a novel Formal Agent-Based Simulation framework (FABS).
Agent-based computing from multi-agent systems to agent-based Models: a visual survey
Agent-Based Computing is a diverse research domain concerned with the building of intelligent software based on the concept of "agents".
Verification & Validation of Agent Based Simulations using the VOMAS (Virtual Overlay Multi-agent System) approach
Our technique, which allows for the validation of agent based simulations uses VOMAS: a Virtual Overlay Multi-agent System.
Agent based Tools for Modeling and Simulation of Self-Organization in Peer-to-Peer, Ad-Hoc and other Complex Networks
Agent-based modeling and simulation tools provide a mature platform for development of complex simulations.
Benchmarking Multimodal Sentiment Analysis
We propose a framework for multimodal sentiment analysis and emotion recognition using convolutional neural network-based feature extraction from text and visual modalities.
A new recurrent neural network based predictive model for Faecal Calprotectin analysis: A retrospective study
Faecal Calprotectin (FC) is a surrogate marker for intestinal inflammation, termed Inflammatory Bowel Disease (IBD), but not for cancer.
Group Sparse Regularization for Deep Neural Networks
In this paper, we consider the joint task of simultaneously optimizing (i) the weights of a deep neural network, (ii) the number of neurons for each hidden layer, and (iii) the subset of active input features (i. e., feature selection).
Affective Common Sense Knowledge Acquisition for Sentiment Analysis
Thanks to the advent of Web 2. 0, the potential for opinion sharing today is unmatched in history.
