Knowledge Fused Recognition: Fusing Hierarchical Knowledge for Image Recognition through Quantitative Relativity Modeling and Deep Metric Learning

• URL: http://arxiv.org/abs/2407.20600v1
• Date: Tue, 30 Jul 2024 07:24:33 GMT
• Title: Knowledge Fused Recognition: Fusing Hierarchical Knowledge for Image Recognition through Quantitative Relativity Modeling and Deep Metric Learning
• Authors: Yunfeng Zhao, Huiyu Zhou, Fei Wu, Xifeng Wu,
• Abstract summary: We propose a novel deep metric learning based method to fuse hierarchical prior knowledge about image classes. Existing deep metric learning incorporated image classification mainly exploits qualitative relativity between image classes. A new triplet loss function term that exploits quantitative relativity and aligns distances in model latent space with those in knowledge space is also proposed and incorporated in the proposed dual-modality fusion method.
• Score: 18.534970504136254
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image recognition is an essential baseline for deep metric learning. Hierarchical knowledge about image classes depicts inter-class similarities or dissimilarities. Effective fusion of hierarchical knowledge about image classes to enhance image recognition remains a challenging topic to advance. In this paper, we propose a novel deep metric learning based method to effectively fuse hierarchical prior knowledge about image classes and enhance image recognition performances in an end-to-end supervised regression manner. Existing deep metric learning incorporated image classification mainly exploits qualitative relativity between image classes, i.e., whether sampled images are from the same class. A new triplet loss function term that exploits quantitative relativity and aligns distances in model latent space with those in knowledge space is also proposed and incorporated in the proposed dual-modality fusion method. Experimental results indicate that the proposed method enhanced image recognition performances and outperformed baseline and existing methods on CIFAR-10, CIFAR-100, Mini-ImageNet, and ImageNet-1K datasets.

Related papers

• Learn and Search: An Elegant Technique for Object Lookup using Contrastive Learning [6.912349403119665]
  "Learn and Search" is a novel approach for object lookup that leverages the power of contrastive learning to enhance the efficiency and effectiveness of retrieval systems. "Learn and Search" achieves superior Similarity Grid Accuracy, showcasing its efficacy in discerning regions of utmost similarity within an image.
  arXiv  Detail & Related papers  (2024-03-12T00:58:19Z)
• Introspective Deep Metric Learning [91.47907685364036]
  We propose an introspective deep metric learning framework for uncertainty-aware comparisons of images. The proposed IDML framework improves the performance of deep metric learning through uncertainty modeling.
  arXiv  Detail & Related papers  (2023-09-11T16:21:13Z)
• Mitigating Bias: Enhancing Image Classification by Improving Model Explanations [9.791305104409057]
  Deep learning models tend to rely heavily on simple and easily discernible features in the background of images. We introduce a mechanism that encourages the model to allocate sufficient attention to the foreground. Our findings highlight the importance of foreground attention in enhancing model understanding and representation of the main concepts within images.
  arXiv  Detail & Related papers  (2023-07-04T04:46:44Z)
• Introspective Deep Metric Learning for Image Retrieval [80.29866561553483]
  We argue that a good similarity model should consider the semantic discrepancies with caution to better deal with ambiguous images for more robust training. We propose to represent an image using not only a semantic embedding but also an accompanying uncertainty embedding, which describes the semantic characteristics and ambiguity of an image, respectively. The proposed IDML framework improves the performance of deep metric learning through uncertainty modeling and attains state-of-the-art results on the widely used CUB-200-2011, Cars196, and Stanford Online Products datasets.
  arXiv  Detail & Related papers  (2022-05-09T17:51:44Z)
• LEAD: Self-Supervised Landmark Estimation by Aligning Distributions of Feature Similarity [49.84167231111667]
  Existing works in self-supervised landmark detection are based on learning dense (pixel-level) feature representations from an image. We introduce an approach to enhance the learning of dense equivariant representations in a self-supervised fashion. We show that having such a prior in the feature extractor helps in landmark detection, even under drastically limited number of annotations.
  arXiv  Detail & Related papers  (2022-04-06T17:48:18Z)
• Hybrid Optimized Deep Convolution Neural Network based Learning Model for Object Detection [0.0]
  Object identification is one of the most fundamental and difficult issues in computer vision. In recent years, deep learning-based object detection techniques have grabbed the public's interest. In this study, a unique deep learning classification technique is used to create an autonomous object detecting system. The suggested framework has a detection accuracy of 0.9864, which is greater than current techniques.
  arXiv  Detail & Related papers  (2022-03-02T04:39:37Z)
• Contrastive Object Detection Using Knowledge Graph Embeddings [72.17159795485915]
  We compare the error statistics of the class embeddings learned from a one-hot approach with semantically structured embeddings from natural language processing or knowledge graphs. We propose a knowledge-embedded design for keypoint-based and transformer-based object detection architectures.
  arXiv  Detail & Related papers  (2021-12-21T17:10:21Z)
• Unifying Remote Sensing Image Retrieval and Classification with Robust Fine-tuning [3.6526118822907594]
  We aim at unifying remote sensing image retrieval and classification with a new large-scale training and testing dataset, SF300. We show that our framework systematically achieves a boost of retrieval and classification performance on nine different datasets compared to an ImageNet pretrained baseline.
  arXiv  Detail & Related papers  (2021-02-26T11:01:30Z)
• Distilling Localization for Self-Supervised Representation Learning [82.79808902674282]
  Contrastive learning has revolutionized unsupervised representation learning. Current contrastive models are ineffective at localizing the foreground object. We propose a data-driven approach for learning in variance to backgrounds.
  arXiv  Detail & Related papers  (2020-04-14T16:29:42Z)
• DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learning [122.51237307910878]
  We develop methods for few-shot image classification from a new perspective of optimal matching between image regions. We employ the Earth Mover's Distance (EMD) as a metric to compute a structural distance between dense image representations. To generate the important weights of elements in the formulation, we design a cross-reference mechanism.
  arXiv  Detail & Related papers  (2020-03-15T08:13:16Z)

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.