Research on Optimization of Natural Language Processing Model Based on Multimodal Deep Learning

• URL: http://arxiv.org/abs/2406.08838v1
• Date: Thu, 13 Jun 2024 06:03:59 GMT
• Title: Research on Optimization of Natural Language Processing Model Based on Multimodal Deep Learning
• Authors: Dan Sun, Yaxin Liang, Yining Yang, Yuhan Ma, Qishi Zhan, Erdi Gao,
• Abstract summary: This project intends to study the image representation based on attention mechanism and multimodal data. By adding multiple pattern layers to the model, the semantic and hidden layers of image content are integrated. The word vector is quantified by the Word2Vec method and then evaluated by a word embedding convolutional neural network.
• Score: 0.036651088217486416
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This project intends to study the image representation based on attention mechanism and multimodal data. By adding multiple pattern layers to the attribute model, the semantic and hidden layers of image content are integrated. The word vector is quantified by the Word2Vec method and then evaluated by a word embedding convolutional neural network. The published experimental results of the two groups were tested. The experimental results show that this method can convert discrete features into continuous characters, thus reducing the complexity of feature preprocessing. Word2Vec and natural language processing technology are integrated to achieve the goal of direct evaluation of missing image features. The robustness of the image feature evaluation model is improved by using the excellent feature analysis characteristics of a convolutional neural network. This project intends to improve the existing image feature identification methods and eliminate the subjective influence in the evaluation process. The findings from the simulation indicate that the novel approach has developed is viable, effectively augmenting the features within the produced representations.

Related papers

• Base and Exponent Prediction in Mathematical Expressions using Multi-Output CNN [2.4366097951781795]
  This research presents a simplified yet effective approach to predicting both the base and exponent from images of mathematical expressions using a multi-output Convolutional Neural Network (CNN) The model is trained on 10,900 synthetically generated images containing exponent expressions, incorporating random noise, font size variations, and blur intensity to simulate real-world conditions. The experimental results indicate that the model achieves high accuracy in predicting the base and exponent values, proving the efficacy of this approach in handling noisy and varied input images.
  arXiv  Detail & Related papers  (2024-07-20T19:23:40Z)
• Harnessing Diffusion Models for Visual Perception with Meta Prompts [68.78938846041767]
  We propose a simple yet effective scheme to harness a diffusion model for visual perception tasks. We introduce learnable embeddings (meta prompts) to the pre-trained diffusion models to extract proper features for perception. Our approach achieves new performance records in depth estimation tasks on NYU depth V2 and KITTI, and in semantic segmentation task on CityScapes.
  arXiv  Detail & Related papers  (2023-12-22T14:40:55Z)
• Advanced Image Segmentation Techniques for Neural Activity Detection via C-fos Immediate Early Gene Expression [0.0]
  We develop a novel workflow for the segmentation process involving Convolutional Neural Networks (CNNs) and the Unet model. We demonstrate the effectiveness of our method in distinguishing areas with significant C-fos expression from normal tissue areas.
  arXiv  Detail & Related papers  (2023-12-13T14:36:16Z)
• Pixel-Inconsistency Modeling for Image Manipulation Localization [63.54342601757723]
  Digital image forensics plays a crucial role in image authentication and manipulation localization. This paper presents a generalized and robust manipulation localization model through the analysis of pixel inconsistency artifacts. Experiments show that our method successfully extracts inherent pixel-inconsistency forgery fingerprints.
  arXiv  Detail & Related papers  (2023-09-30T02:54:51Z)
• DeepDC: Deep Distance Correlation as a Perceptual Image Quality Evaluator [53.57431705309919]
  ImageNet pre-trained deep neural networks (DNNs) show notable transferability for building effective image quality assessment (IQA) models. We develop a novel full-reference IQA (FR-IQA) model based exclusively on pre-trained DNN features. We conduct comprehensive experiments to demonstrate the superiority of the proposed quality model on five standard IQA datasets.
  arXiv  Detail & Related papers  (2022-11-09T14:57:27Z)
• Joint Learning of Deep Texture and High-Frequency Features for Computer-Generated Image Detection [24.098604827919203]
  We propose a joint learning strategy with deep texture and high-frequency features for CG image detection. A semantic segmentation map is generated to guide the affine transformation operation. The combination of the original image and the high-frequency components of the original and rendered images are fed into a multi-branch neural network equipped with attention mechanisms.
  arXiv  Detail & Related papers  (2022-09-07T17:30:40Z)
• Summarize and Search: Learning Consensus-aware Dynamic Convolution for Co-Saliency Detection [139.10628924049476]
  Humans perform co-saliency detection by first summarizing the consensus knowledge in the whole group and then searching corresponding objects in each image. Previous methods usually lack robustness, scalability, or stability for the first process and simply fuse consensus features with image features for the second process. We propose a novel consensus-aware dynamic convolution model to explicitly and effectively perform the "summarize and search" process.
  arXiv  Detail & Related papers  (2021-10-01T12:06:42Z)
• Detection and Captioning with Unseen Object Classes [12.894104422808242]
  Test images may contain visual objects with no corresponding visual or textual training examples. We propose a detection-driven approach based on a generalized zero-shot detection model and a template-based sentence generation model. Our experiments show that the proposed zero-shot detection model obtains state-of-the-art performance on the MS-COCO dataset.
  arXiv  Detail & Related papers  (2021-08-13T10:43:20Z)
• Comparative evaluation of CNN architectures for Image Caption Generation [1.2183405753834562]
  We have evaluated 17 different Convolutional Neural Networks on two popular Image Caption Generation frameworks. We observe that model complexity of Convolutional Neural Network, as measured by number of parameters, and the accuracy of the model on Object Recognition task does not necessarily co-relate with its efficacy on feature extraction for Image Caption Generation task.
  arXiv  Detail & Related papers  (2021-02-23T05:43:54Z)
• NAS-DIP: Learning Deep Image Prior with Neural Architecture Search [65.79109790446257]
  Recent work has shown that the structure of deep convolutional neural networks can be used as a structured image prior. We propose to search for neural architectures that capture stronger image priors. We search for an improved network by leveraging an existing neural architecture search algorithm.
  arXiv  Detail & Related papers  (2020-08-26T17:59:36Z)
• Learning to Compose Hypercolumns for Visual Correspondence [57.93635236871264]
  We introduce a novel approach to visual correspondence that dynamically composes effective features by leveraging relevant layers conditioned on the images to match. The proposed method, dubbed Dynamic Hyperpixel Flow, learns to compose hypercolumn features on the fly by selecting a small number of relevant layers from a deep convolutional neural network.
  arXiv  Detail & Related papers  (2020-07-21T04:03:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.