Abstract: Personal exercise health assessment demands accurate, interpretable, and adaptive modeling of complex physiological dynamics. To address the limitations of conventional approaches, we ...
Multimodal Learning, Deep Learning, Financial Statement Analysis, LSTM, FinBERT, Financial Text Mining, Automated Interpretation, Financial Analytics Share and Cite: Wandwi, G. and Mbekomize, C. (2025 ...
Despite notable progress in deep learning, change detection (CD) in remote sensing images continues to pose significant challenges, especially for multimodal datasets due to intrinsic differences [1].
V, a multimodal model that has introduced native visual function calling to bypass text conversion in agentic workflows.
CLIP is one of the most important multimodal foundational models today, aligning visual and textual signals into a shared feature space using a simple contrastive learning loss on large-scale ...
Chinese AI startup Zhipu AI aka Z.ai has released its GLM-4.6V series, a new generation of open-source vision-language models ...
Average decoding scores for modality-agnostic decoders (green), compared to modality-specific decoders trained on data from subjects viewing images (orange) or on data from subjects viewing captions ...
An AI system developed at NYU Abu Dhabi can predict solar wind conditions four days ahead by analyzing detailed images of the Sun. The improved accuracy may help shield satellites, power grids, and ...
Converting protein tertiary structure into discrete tokens via vector-quantized variational autoencoders (VQ-VAEs) creates a language of 3D geometry and provides a natural interface between sequence ...
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