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Align vision-language semantics by multi-task learning for multi-modal summarization
Most current multi-modal summarization methods follow a cascaded manner, where an off-the-shelf object detector is first used to extract visual...
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MutualFormer: Multi-modal Representation Learning via Cross-Diffusion Attention
Aggregating multi-modal data to obtain reliable data representation attracts more and more attention. Recent studies demonstrate that Transformer...
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Modelling flight trajectories with multi-modal generative adversarial imitation learning
Models of aircraft trajectories become important components of systems supporting the trajectory based operations paradigm: trajectory predictability...
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Multi-modal Hash Learning Efficient Multimedia Retrieval and Recommendations
This book systemically presents key concepts of multi-modal hashing technology, recent advances on large-scale efficient multimedia search and...
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PointCMC: cross-modal multi-scale correspondences learning for point cloud understanding
Existing cross-modal frameworks have achieved impressive performance in point cloud object representations learning, where a 2D image encoder is...
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UniMod1K: Towards a More Universal Large-Scale Dataset and Benchmark for Multi-modal Learning
The emergence of large-scale high-quality datasets has stimulated the rapid development of deep learning in recent years. However, most computer...
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UaMC: user-augmented conversation recommendation via multi-modal graph learning and context mining
Conversation Recommender System (CRS) engage in multi-turn conversations with users and provide recommendations through responses. As user...
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MMPL-Net: multi-modal prototype learning for one-shot RGB-D segmentation
For one-shot segmentation, prototype learning is extensively used. However, using only one RGB prototype to represent all information in the support...
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Federated learning inspired privacy sensitive emotion recognition based on multi-modal physiological sensors
Traditional machine learning classifiers can automatically evaluate human behaviour and emotion recognition tasks. However, prior research work does...
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Bayesian mixture variational autoencoders for multi-modal learning
This paper provides an in-depth analysis on how to effectively acquire and generalize cross-modal knowledge for multi-modal learning....
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Human Gait Recognition Based on Frontal-View Walking Sequences Using Multi-modal Feature Representations and Learning
Despite that much progress has been reported in gait recognition, most of these existing works adopt lateral-view parameters as gait features, which...
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Collaboration based multi-modal multi-label learning
Complex objects can be represented as multiple modal features and associated with multiple labels. The major challenge of complex object...
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Deep learning based object detection from multi-modal sensors: an overview
Object detection is an important problem and has a wide range of applications. In recent years, deep learning based object detection with...
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CMC-MMR: multi-modal recommendation model with cross-modal correction
Multi-modal recommendation using multi-modal features (e.g., image and text features) has received significant attention and has been shown to have...
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SSDMM-VAE: variational multi-modal disentangled representation learning
Multi-modal learning aims at simultaneously modelling data from several modalities such as image, text and speech. The goal is to simultaneously...
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Lightweight Multi-modal Representation Learning for RGB Salient Object Detection
The task of salient object detection (SOD) often faces various challenges such as complex backgrounds and low appearance contrast. Depth information,...
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MADMM: microservice system anomaly detection via multi-modal data and multi-feature extraction
Accurately detecting anomalies in microservice systems is crucial to avoid system failures and economic losses for users. Existing approaches detect...
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Re-transfer learning and multi-modal learning assisted early diagnosis of Alzheimer’s disease
Nowadays more and more elderly people are suffering from Alzheimer’s disease (AD). Finely recognizing mild cognitive impairment (MCI) in early stage...
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Multi-modal bilinear fusion with hybrid attention mechanism for multi-label skin lesion classification
Skin cancer is one of the most prevalent malignancies in the world. Deep learning-based methods have been successfully used for skin disease...
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Multi-modal Graph and Sequence Fusion Learning for Recommendation
Multi-modal recommendation aims to leverage multi-modal information for mining users’ latent preferences. Existing multi-modal recommendation...