Foundations & trends in multimodal machine learning: Principles, challenges, and open questions
Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …
computer agents with intelligent capabilities such as understanding, reasoning, and learning …
Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions
Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …
computer agents with intelligent capabilities such as understanding, reasoning, and learning …
A brief survey on semantic segmentation with deep learning
S Hao, Y Zhou, Y Guo - Neurocomputing, 2020 - Elsevier
Semantic segmentation is a challenging task in computer vision. In recent years, the
performance of semantic segmentation has been greatly improved by using deep learning …
performance of semantic segmentation has been greatly improved by using deep learning …
Neural collaborative filtering
In recent years, deep neural networks have yielded immense success on speech
recognition, computer vision and natural language processing. However, the exploration of …
recognition, computer vision and natural language processing. However, the exploration of …
Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention
Multimedia content is dominating today's Web information. The nature of multimedia user-
item interactions is 1/0 binary implicit feedback (eg, photo likes, video views, song …
item interactions is 1/0 binary implicit feedback (eg, photo likes, video views, song …
Deep item-based collaborative filtering for top-n recommendation
Item-based Collaborative Filtering (ICF) has been widely adopted in recommender systems
in industry, owing to its strength in user interest modeling and ease in online …
in industry, owing to its strength in user interest modeling and ease in online …
Survey on deep multi-modal data analytics: Collaboration, rivalry, and fusion
Y Wang - ACM Transactions on Multimedia Computing …, 2021 - dl.acm.org
With the development of web technology, multi-modal or multi-view data has surged as a
major stream for big data, where each modal/view encodes individual property of data …
major stream for big data, where each modal/view encodes individual property of data …
Cross-modal retrieval with CNN visual features: A new baseline
Recently, convolutional neural network (CNN) visual features have demonstrated their
powerful ability as a universal representation for various recognition tasks. In this paper …
powerful ability as a universal representation for various recognition tasks. In this paper …
Deep multimodal distance metric learning using click constraints for image ranking
How do we retrieve images accurately? Also, how do we rank a group of images precisely
and efficiently for specific queries? These problems are critical for researchers and …
and efficiently for specific queries? These problems are critical for researchers and …
Modality-invariant asymmetric networks for cross-modal hashing
Cross-modal hashing has garnered considerable attention and gained great success in
many cross-media similarity search applications due to its prominent computational …
many cross-media similarity search applications due to its prominent computational …