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The five core challenges in multimodal ML are – representation, translation, alignment, fusion, and co-learning. Let's start looking through each of these individually.
Multimodal machine learning aims to build models that can process and relate information from multiple modalities. From early research on audio-visual speech.
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Jun 24, 2021 · The five core challenges in the field of multimodal ML are – representation, translation, alignment, fusion, and co-learning.
Jul 29, 2021 · We present the comprehensive taxonomy of multimodal co-learning based on the challenges addressed by co-learning and associated implementations.
Challenges and applications in multimodal machine learning · Contents. The Handbook of Multimodal-Multisensor Interfaces: Signal Processing, Architectures, and ...
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Jan 17, 2024 · This paper provides an overview of multimodal machine learning, highlighting recent advancements and persistent challenges.
Aug 7, 2024 · Core Challenges · 1. Representation · 2. Aligment · 3. Reasoning · 4. Generation · 5. Transference · 6. Quantification.
We request contributions presenting techniques that will contribute to addressing multimodal machine learning challenges.
Feb 9, 2023 · This paper reviews recent advancements in the challenges of MML, namely: representation, translation, alignment, fusion and co-learning, and presents the gaps ...
The fourth challenge is fusion, which has a broad range of applications. Fusion joins information from multiple modalities for prediction [6]. Multimodal fusion ...