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Multimodal learning with graphs
Artificial intelligence for graphs has achieved remarkable success in modelling complex
systems, ranging from dynamic networks in biology to interacting particle systems in physics …
systems, ranging from dynamic networks in biology to interacting particle systems in physics …
A survey of multi-agent deep reinforcement learning with communication
Communication is an effective mechanism for coordinating the behaviors of multiple agents,
broadening their views of the environment, and to support their collaborations. In the field of …
broadening their views of the environment, and to support their collaborations. In the field of …
Focal: Contrastive learning for multimodal time-series sensing signals in factorized orthogonal latent space
This paper proposes a novel contrastive learning framework, called FOCAL, for extracting
comprehensive features from multimodal time-series sensing signals through self …
comprehensive features from multimodal time-series sensing signals through self …
All in one framework for multimodal re-identification in the wild
Abstract In Re-identification (ReID) recent advancements yield noteworthy progress in both
unimodal and cross-modal retrieval tasks. However the challenge persists in develo** a …
unimodal and cross-modal retrieval tasks. However the challenge persists in develo** a …
Self-weighted contrastive learning among multiple views for mitigating representation degeneration
Recently, numerous studies have demonstrated the effectiveness of contrastive learning
(CL), which learns feature representations by pulling in positive samples while pushing …
(CL), which learns feature representations by pulling in positive samples while pushing …
Geometric-inspired graph-based incomplete multi-view clustering
Multi-view clustering methods group data into different clusters by discovering the
consensus in heterogeneous sources, which however becomes difficult when partial views …
consensus in heterogeneous sources, which however becomes difficult when partial views …
M3AE: Multimodal representation learning for brain tumor segmentation with missing modalities
Multimodal magnetic resonance imaging (MRI) provides complementary information for sub-
region analysis of brain tumors. Plenty of methods have been proposed for automatic brain …
region analysis of brain tumors. Plenty of methods have been proposed for automatic brain …
Contextual augmented global contrast for multimodal intent recognition
Multimodal intent recognition (MIR) aims to perceive the human intent polarity via language
visual and acoustic modalities. The inherent intent ambiguity makes it challenging to …
visual and acoustic modalities. The inherent intent ambiguity makes it challenging to …
Identifiability results for multimodal contrastive learning
Contrastive learning is a cornerstone underlying recent progress in multi-view and
multimodal learning, eg, in representation learning with image/caption pairs. While its …
multimodal learning, eg, in representation learning with image/caption pairs. While its …
Missmodal: Increasing robustness to missing modality in multimodal sentiment analysis
When applying multimodal machine learning in downstream inference, both joint and
coordinated multimodal representations rely on the complete presence of modalities as in …
coordinated multimodal representations rely on the complete presence of modalities as in …