mlsanityData to 7 Oct 2026

Algorithm Research of ELMo Word Embedding and Deep Learning Multimodal Transformer in Image Description

arXiv 2408.06357 ↗First posted 26 Jul 2024cs.CV cs.AIcited by 10

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Abstract (arXiv)

Zero sample learning is an effective method for data deficiency. The existing embedded zero sample learning methods only use the known classes to construct the embedded space, so there is an overfitting of the known classes in the testing process. This project uses category semantic similarity measures to classify multiple tags. This enables it to incorporate unknown classes that have the same meaning as currently known classes into the vector space when it is built. At the same time, most of the existing zero sample learning algorithms directly use the depth features of medical images as input, and the feature extraction process does not consider semantic information. This project intends to take ELMo-MCT as the main task and obtain multiple visual features related to the original image through self-attention mechanism. In this paper, a large number of experiments are carried out on three zero-shot learning reference datasets, and the best harmonic average accuracy is obtained compared with the most advanced algorithms.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 12 of its 27 references that are in the map. First posted on arXiv 26 Jul 2024.

Builds on

12 of 27 references.

References · 12

  1. 2020A Review of Generalized Zero-Shot Learning Methodscited by 159
  2. 2024Survival Prediction Across Diverse Cancer Types Using Neural Networkscited by 80
  3. 2023Improving Depth Gradient Continuity in Transformers: A Comparative Study on Monocular Depth Estimation with CNNcited by 35
  4. 2020A Comprehensive Survey on Word Representation Models: From Classical to State-Of-The-Art Word Representation Language Modelscited by 30
  5. 2015Top-down Tree Long Short-Term Memory Networkscited by 12
  6. 2024Research on Image Recognition Technology Based on Multimodal Deep Learningcited by 11
  7. 2024Exploration of Attention Mechanism-Enhanced Deep Learning Models in the Mining of Medical Textual Datacited by 11
  8. 2024Research on Intelligent Aided Diagnosis System of Medical Image Based on Computer Deep Learningcited by 8
  9. 2024Integrating Medical Imaging and Clinical Reports Using Multimodal Deep Learning for Advanced Disease Analysiscited by 8
  10. 2024Investigation of Customized Medical Decision Algorithms Utilizing Graph Neural Networkscited by 5
  11. 2024Advancing Financial Risk Prediction Through Optimized LSTM Model Performance and Comparative Analysiscited by 5
  12. 2017Fine-tuning deep CNN models on specific MS COCO categoriescited by 1

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Cited by year

Citing papers per year: 2024: 10.

10 papers.

  1. 2024Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generationcited by 13
  2. 2024Wasserstein Distance-Weighted Adversarial Network for Cross-Domain Credit Risk Assessmentcited by 11
  3. 2024Contrastive Learning for Knowledge-Based Question Generation in Large Language Modelscited by 8
  4. 2024Applying Hybrid Graph Neural Networks to Strengthen Credit Risk Analysiscited by 8
  5. 2024A Recommendation Model Utilizing Separation Embedding and Self-Attention for Feature Miningcited by 8
  6. 2024Deep Learning for Medical Text Processing: BERT Model Fine-Tuning and Comparative Studycited by 8
  7. 2024Optimizing News Text Classification with Bi-LSTM and Attention Mechanism for Efficient Data Processingcited by 7
  8. 2024Deep Learning-Based Channel Squeeze U-Structure for Lung Nodule Detection and Segmentationcited by 6
  9. 2024Graph Neural Network Framework for Sentiment Analysis Using Syntactic Featurecited by 6
  10. 2024Optimizing Retrieval-Augmented Generation with Elasticsearch for Enhanced Question-Answering Systemscited by 5