Multimodal entity linking (MEL) is an emerging research field which uses both textual and visual information to map an ambiguous mention to an entity in a knowledge base (KB). However, images do not always help, which may also backfire if they are irrelevant to the textual content at all....
1. Introduction Multimodal information dramatically increases the ef- This work was supported in part by IIT Roorkee, India under grant FIG-100874. fectiveness of communication. Whether a news article or a textbook, multiple modalities such as text, image, audio an...
Full size image However, this task usually faces the following challenges: Severe data loss: In practical applications, due to the extensive damage of the Terracotta Warriors themselves or limited scanning view of the device, the collected point cloud data often contains only parts of the objects....
Unet++: A nested u-net architecture for medical image segmentation. In Proceedings of the Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with ...
Multimodal deep learning for biomedical data fusion: a review. Brief Bioinform. 2022;23(2):569. Article Google Scholar Luo L, Lai P-T, Wei C-H, Arighi CN, Lu Z. Biored: a rich biomedical relation extraction dataset. Brief Bioinform. 2022;23(5):282. Article Google Scholar Xing R,...
Mark yourself: Road marking segmentation via weakly-supervised annotations from multimodal data. IEEE Int. Conf. Robot. Autom. (ICRA) 2018, 1863–1870 (2018). Google Scholar Neven, D., De Brabandere, B., Georgoulis, S., Proesmans, M. & Van Gool, L. Towards end-to-end lane ...
ultimately providing detailed and accurate understanding of cellular interactions. In spite of challenges like long-distance communication and multimodal data amalgamation, DeepTalk stands as a crucial tool for unraveling the intricacies of CCC by probing the cell-to-cell dialog and visually presenting th...
Full size image Task scheduling model It is postulated that multiple edge nodes concurrently handle tasks, all the terminal devices in the system are located in the same production line, the amount of tasks is large enough and follow the principle of First Input First Output (FIFO). We then ...
Consequently, we propose MFCD-Net, a 3D multimodal fusion network based on cross attention, which takes the multi-angle reflectance image, polarization image Q (Stokes vector Q), and polarization image U (Stokes vector U) as inputs for cloud detection. First, we use angle as the third dime...
(ii) A model that provides a matching score between an audio file and a text, for which we use a multimodal matching network called ImageBind, and (iii) A text classifier, trained using a dataset we collected automatically by instructing GPT-4 with prompts designed to direct the generation ...