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Cross-Modal Center Loss for 3D Cross-Modal Retrieval
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021Cross-modal retrieval aims to learn discriminative and modal-invariant features for data from different modalities. Unlike the existing methods which usually learn from the features extracted by offline networks, in this paper, we propose an approach to jointly train the components of cross-modal retrieval framework with metadata, and enable the ...
Longlong Jing +3 more
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HCMSL: Hybrid Cross-modal Similarity Learning for Cross-modal Retrieval
ACM Transactions on Multimedia Computing, Communications, and Applications, 2021The purpose of cross-modal retrieval is to find the relationship between different modal samples and to retrieve other modal samples with similar semantics by using a certain modal sample. As the data of different modalities presents heterogeneous low-level feature and semantic-related high-level features, the main problem of cross-modal retrieval is ...
Chengyuan Zhang 0001 +4 more
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Cross-Modal Transmission Strategy
IEEE Transactions on Circuits and Systems for Video Technology, 2022Multi-modal services, typically integrated by visual, audio, and haptic signals, have been considered as promising killer services in 5G and beyond 5G era. However, due to essential difference among these signals, how to guarantee quality of multi-modal services is a significant technical challenge.
Xin Wei 0001 +2 more
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Cross-Modal Collaborative Communications
IEEE Wireless Communications, 2020It is well known that cross-modal services, including audio, video, and haptic signals, will inevitably become the mainstream of multimedia applications. However, because there are significant differences among these three kinds of signals in terms of transmission delay, jitter, and reliability, how to effectively transmit and process them is an ...
Liang Zhou 0002 +3 more
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Discriminant Cross-modal Hashing
Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval, 2016Hashing based methods have attracted considerable attention for efficient cross-modal retrieval on large-scale multimedia data. The core problem of cross-modal hashing is how to effectively integrate heterogeneous features from different modalities to learn hash functions using available supervising information, e.g., class labels.
Xu, Xing +3 more
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Pattern Recognition, 2018
Abstract In this paper, we present a new cross-modal discrete hashing (CMDH) approach to learn compact binary codes for cross-modal multimedia search. Unlike most existing cross-modal hashing methods which usually relax the optimization objective function to obtain hash codes, we develop a discrete optimization framework to jointly learn binary codes
Venice Erin Liong +2 more
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Abstract In this paper, we present a new cross-modal discrete hashing (CMDH) approach to learn compact binary codes for cross-modal multimedia search. Unlike most existing cross-modal hashing methods which usually relax the optimization objective function to obtain hash codes, we develop a discrete optimization framework to jointly learn binary codes
Venice Erin Liong +2 more
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2006
We consider strategies for reducing ambiguity in multi-modal data, particularly in the domain of images and text. Large data sets containing images with associated text (and vice versa) are readily available, and recent work has exploited such data to learn models for linking visual elements to semantics.
Kobus Barnard +3 more
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We consider strategies for reducing ambiguity in multi-modal data, particularly in the domain of images and text. Large data sets containing images with associated text (and vice versa) are readily available, and recent work has exploited such data to learn models for linking visual elements to semantics.
Kobus Barnard +3 more
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Regularised Cross-Modal Hashing
Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015In this paper we propose Regularised Cross-Modal Hashing (RCMH) a new cross-modal hashing model that projects annotation and visual feature descriptors into a common Hamming space. RCMH optimises the hashcode similarity of related data-points in the annotation modality using an iterative three-step hashing algorithm: in the first step each training ...
Sean Moran, Victor Lavrenko
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Development of cross‐modal processing
WIREs Cognitive Science, 2009AbstractThe ability to process and integrate cross‐modal input is important for many everyday tasks. The current paper reviews theoretical and empirical work examining cross‐modal processing with a focus on recent findings examining infants' and children's processing of arbitrary auditory–visual pairings.
Christopher W, Robinson +1 more
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Cross-modal plasticity and deafferentation
Cognitive Processing, 2004To interact successfully with the environment and to compensate for environmental challenges, the human brain must integrate information originating in different sensory modalities. Such integration occurs in non-primary associative regions of the human brain.
Friedhelm Hummel +2 more
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