2026· International Conference on Language Resources and Evaluation· pp. 9129-9138· 0 citations· 34 references
Computer Science
TL;DR
This work proposes a simple yet effective lightweight framework explicitly designed for multimodal classification tasks, utilising the early fusion method combined with a contrastive learning approach, which achieves performance comparable to the most competitive heavyweight state-of-the-art models and even outperforms them.
: Multimodal learning has emerged as a critical paradigm in artificial intelligence, enabling systems to integrate information from diverse data sources such as text, images, audio, and video. However, effectively fusing heterogeneous modalities remains challenging due to differences in feature dimensions, semantic rep...
K. Bharathi, G. S. Reddy· International Journal of Dru...· 0 citations
A cross-modal representation learning framework that aligns heterogeneous modalities within a shared latent representation space and exhibits strong robustness under missing modality conditions, with significantly lower performance degradation compared to baseline approaches is proposed.
I. Ibrahim, Marwan Alshar'e, I. Sanjaya et al.· Journal of Data Science· 0 citations
In recent years, multimodal deep learning has made great progress, enabling artificial intelligence to learn across text, audio, images, video, and other data. Unlike earlier unimodal deep learning models trained on limited data, new multimodal models are trained on large collections of mixed media and achieve strong r...
Azhar A. Hadi, K. P. Supreethi· Discover Informatics· 0 citations
Making prediction with multimodal data is widely used in diverse scenarios. Existing multimodal fusion models, once deployed, can only handle predefined modalities (e.g., vision, text and audio) and single task, making it difficult to quickly adapt to new downstream applications. Therefore, a natural yet aggressive que...
Hui-Zi Cui, Zong-Bo Han, Chen Ding et al.· 0 citations
Large-scale vision-language pretrained models like CLIP, paired with parameter-efficient fine-tuning techniques, have emerged as promising solutions for image-to-video transfer in video action recognition. However, existing methods often prioritize strong supervised performance at the expense of transferability and gen...
Meng-Meng Wang, Ze-Yi Huang, Bo-Yuan Jiang et al.· IEEE Transactions on Pattern...· 0 citations
Multi-modal large language models (MLLMs) achieve strong modality understanding by pairing a large language model (LLM) with an encoder for a target modality such as vision, video, or audio. However, improving an MLLM's capability for a given modality typically requires additional training on large modality-specific da...
H. Seo, Byung Hyun Lee, Minjun Kim et al.· 0 citations
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