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  1. Only showing results from arxiv.org

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  2. Nov 6, 2024Word embeddings and language models have transformed natural language processing (NLP) by facilitating the representation of linguistic elements in continuous vector spaces. This review visits foundational concepts such as the distributional hypothesis and contextual similarity, tracing the evolution from sparse representations like one-hot encoding to dense embeddings including Word2Vec ...
  3. Nov 6, 2024The distributional hypothesis, a cornerstone of numerous word embedding techniques, posits that words appearing in similar contexts tend to have similar meanings [7]. This hypothesis allows for representing words as vectors in a continuous space, where semantic similarity is reflected by vector proximity [8].
  4. Nov 26, 2024This survey offers a comprehensive review of recent advancements in multimodal alignment and fusion within machine learning, spurred by the growing diversity of data types such as text, images, audio, and video. Multimodal integration enables improved model accuracy and broader applicability by leveraging complementary information across different modalities, as well as facilitating knowledge ...
  5. Contextual Similarity [10]-[12] Distributional Hypothesis [7]-[9] Fig. 1. Taxonomy of Word Embeddings posits that words appearing in similar contexts tend to have similar meanings [7]. This hypothesis allows for representing words as vectors in a continuous space, where semantic similarity is reflected by vector proximity [8].
  6. of research interest, both in academia, and in industry. Multimodal fusion entails the combination of information from a set of different types of sensors. Exploiting complementary information from different sensors, we show that target detection and classification problems can greatly benefit from this fusion approach and result in a performance increase. To achieve this gain, the information ...
    Author:Siddharth Roheda, Hamid Krim, Benjamin S. RigganPublished:2021
  7. ar5iv.labs.arxiv.org

    This challenge is exemplified by algorithms such as co-training, multimodal representation learning, conceptual grounding, and zero shot learning (ZSL) and has found many applications in visual classification, action recognition, audio-visual speech recognition, and semantic similarity estimation.
  8. In many applications involving multi-media data, the definition of similarity between items is integral to several key tasks, e.g., nearest-neighbor retrieval, classification, and recommendation. Data in such regimes typically exhibits multiple modalities, such as acoustic and visual content of video. Integrating such heterogeneous data to form a holistic similarity space is therefore a key ...
  9. ts into contributions are: We propose a plug-in framework WisdoM, leveraging the LVLM to generate explicit con-textual world knowledge, to enhance the mul-timodal sentiment analysis ability. To achieve wise knowledge fusion, we intro-duce a novel contextual fusion mechanism to mitigate the impact of noise in the context.
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