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  1. [8], image labeling and retrieval [9], etc. The term multimodal fusion is used to indicate the integration of information from multiple modalities. In this work, we fuse text-, audio- and image-based models for the estimation of word semantic similarity. Two main fusion methods are employed here, namely middle and late fusion.
  2. semanticscholar.org

    This work estimates multimodal word representations via the fusion of auditory and visual modalities with the text modality through middle and late fusion of representations with modality weights assigned to each of the unimodal representations. Traditional semantic models are disembodied from the human perception and action. In this work, we attempt to address this problem by grounding ...
  3. pure.unic.ac.cy

    Sensory-Aware Multimodal Fusion for Word Semantic Similarity Estimation. / Paraskevopoulos, George; Karamanolakis, Giannis; Iosif, Elias et al. 2017. Paper presented at MultiLearn Workshop, Kos island, Greece. Research output: Contribution to conference › Paper › peer-review
    Author:George Paraskevopoulos, Giannis Karamanolakis, Elias Iosif, Aggelos Pikrakis, Alexandros PotamianosPublished:2017
  4. georgepar.github.io

    Sensory-Aware Multimodal Fusion for Word Semantic Similarity Estimation Georgios Paraskevopoulos, Giannis Karamanolakis, Elias Iosif, Aggelos Pikrakis, Alexandros Potamianos MultiLearn2017: Multimodal Processing, Modelingand Learning for Human-Computer/Robot Interaction Workshop 2017. A real-time approach for gesture recognition using the ...
  5. scholar.google.com

    Sentiment-aware multimodal pre-training for multimodal sentiment analysis. J Ye, J Zhou, J Tian, R Wang, J Zhou, T Gui, Q Zhang, X Huang ... Propaganda detection using transfer learning and multimodal fusion. ... using traditional similarity measurements and word embedding for semantic textual similarity estimation.
  6. cl.cam.ac.uk

    Multimodal distributional semantics Combining text and visual words Example task: word similarity estimation, e.g. using cosine 1.Feature level fusion: I concatenate textual and visual feature vectors I dimensionality reduction (some approaches) - map the features into the same low dimensional space, e.g. using SVD or NMF
  7. This motivates us to build a multimodal model that can dynamically fuse the semantic representations from different modalities according to different types of words. To that end, we propose three novel dynamic fusion methods to assign importance weights to each modality, in which weights are learned under the weak supervision of word ...
  8. aclanthology.org

    Jan 16, 2025Abstract Multimodal learning is generally expected to make more accurate predictions than text-only analysis. Here, although various methods for fusing multimodal inputs have been proposed for sentiment analysis tasks, we found that they may be inhibiting their fusion methods, which are based on attention-based language models, from learning non-verbal modalities, because non-verbal ones are ...
  9. ncbi.nlm.nih.gov

    National Center for Biotechnology Information

    https://www.ncbi.nlm.nih.gov › pmc › articles › PMC7816238

    The multimodal fusion M of the modalities a and b ... 2014), which was collected with the specific purpose of evaluating visual and semantic similarity in multimodal models. The second dataset was SimVerb‐3500 (Gerz, Vulić ... similarity estimation. Computational Linguistics, 41, 665-695. 10.1162/coli_a_00237 [Google Scholar] ...
  10. nlpr.ia.ac.cn

    ing (i.e., the two words share more similarity in linguistic modality). To achieve the goal of making these two asso-ciation words obtain similar representations, the model will assign more weights to their linguistic vectors. In light of these considerations, we propose three novel dynamic fusion methods to improve multimodal word rep ...

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