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Found 15 papers, 5 papers with code
Joint Physical-Digital Facial Attack Detection Via Simulating Spoofing Clues
SPSC and SDSC augment live samples into simulated attack samples by simulating spoofing clues of physical and digital attacks, respectively, which significantly improve the capability of the model to detect "unseen" attack types.
From Pixel to Slide image: Polarization Modality-based Pathological Diagnosis Using Representation Learning
This structure includes a pathology structure recognition method to predict structures related to thyroid tumors, an encoder-decoder network to extract pixel-level annotation information by learning the feature representations of image blocks, and an attention-based learning mechanism for the final classification task.
A Polarization and Radiomics Feature Fusion Network for the Classification of Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma
Our experimental results underscore the potential of this fusion network as a powerful tool for computer-aided diagnosis of HCC and ICC, showcasing the benefits and prospects of integrating polarization imaging techniques into the current image-intensive digital pathological diagnosis.
A Transformer-Based Model With Self-Distillation for Multimodal Emotion Recognition in Conversations
Emotion recognition in conversations (ERC), the task of recognizing the emotion of each utterance in a conversation, is crucial for building empathetic machines.
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Emotion Recognition in Conversation
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Emotion Recognition in Conversation
Multimodal Emotion Recognition
Learning to Optimise Climate Sensor Placement using a Transformer
In this paper, we introduce a novel sensor placement approach focused on learning improvement heuristics using deep reinforcement learning (RL) methods.
ZRIGF: An Innovative Multimodal Framework for Zero-Resource Image-Grounded Dialogue Generation
To overcome this challenge, we propose an innovative multimodal framework, called ZRIGF, which assimilates image-grounded information for dialogue generation in zero-resource situations.
DeepMA: End-to-end Deep Multiple Access for Wireless Image Transmission in Semantic Communication
Semantic communication is a new paradigm that exploits deep learning models to enable end-to-end communications processes, and recent studies have shown that it can achieve better noise resiliency compared with traditional communication schemes in a low signal-to-noise (SNR) regime.
Exploiting Pairwise Mutual Information for Knowledge-Grounded Dialogue
Correlation integration is designed to fully exploit the pairwise mutual information among dialogue context, knowledge, and responses, while overall integration adopts an integration gate to capture global information.
Gaussian Mixture Model Based Distributionally Robust Optimal Power Flow With CVaR Constraints
This paper proposes a distributionally robust optimal power flow (OPF) model for transmission grids with wind power generation.
Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label
Our weakly-supervised deep learning model can accurately predict the COVID-19 infectious probability in chest CT volumes without the need for annotating the lesions for training.
Skull-RCNN: A CNN-based network for the skull fracture detection
Guiding by the morphological features of the skull, a skeleton-based region proposal method is proposed to make candidate boxes more concentrated in key regions and reduce invalid boxes.
Memetic EDA-Based Approaches to Comprehensive Quality-Aware Automated Semantic Web Service Composition
The objective of this problem is to find a solution with optimized or near-optimized overall QoS and QoSM within polynomial time over a service request.
Evolutionary Multitasking for Semantic Web Service Composition
We also found that the use of the proper neighborhood structure can enhance the effectiveness of our approach.
Composing Distributed Data-intensive Web Services Using a Flexible Memetic Algorithm
Data-intensive Web services, which manipulate and deal with those data, are of great interest to implement data-intensive processes, such as distributed Data-intensive Web Service Composition (DWSC).
Distance-Guided GA-Based Approach to Distributed Data-Intensive Web Service Composition
As a fundamental challenge for service developers, service composition must fulfil functional requirements and optimise Quality of Service (QoS) attributes, simultaneously.
