Semantic Guided Level-Category Hybrid Prediction Network for Hierarchical Image Classification

• URL: http://arxiv.org/abs/2211.12277v3
• Date: Fri, 31 Mar 2023 08:52:12 GMT
• Title: Semantic Guided Level-Category Hybrid Prediction Network for Hierarchical Image Classification
• Authors: Peng Wang, Jingzhou Chen, Yuntao Qian
• Abstract summary: Hierarchical classification (HC) assigns each object with multiple labels organized into a hierarchical structure. We propose a novel semantic guided level-category hybrid prediction network (SGLCHPN) that can jointly perform the level and category prediction in an end-to-end manner.
• Score: 8.456482280676884
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hierarchical classification (HC) assigns each object with multiple labels organized into a hierarchical structure. The existing deep learning based HC methods usually predict an instance starting from the root node until a leaf node is reached. However, in the real world, images interfered by noise, occlusion, blur, or low resolution may not provide sufficient information for the classification at subordinate levels. To address this issue, we propose a novel semantic guided level-category hybrid prediction network (SGLCHPN) that can jointly perform the level and category prediction in an end-to-end manner. SGLCHPN comprises two modules: a visual transformer that extracts feature vectors from the input images, and a semantic guided cross-attention module that uses categories word embeddings as queries to guide learning category-specific representations. In order to evaluate the proposed method, we construct two new datasets in which images are at a broad range of quality and thus are labeled to different levels (depths) in the hierarchy according to their individual quality. Experimental results demonstrate the effectiveness of our proposed HC method.

Related papers

• Category-Extensible Out-of-Distribution Detection via Hierarchical Context Descriptions [35.20091752343433]
  This work introduces two hierarchical contexts, namely perceptual context and spurious context, to carefully describe the precise category boundary. The two contexts hierarchically construct the precise description for a certain category, which is first roughly classifying a sample to the predicted category. The precise descriptions for those categories within the vision-language framework present a novel application: CATegory-EXtensible OOD detection (CATEX)
  arXiv  Detail & Related papers  (2024-07-23T12:53:38Z)
• A Capsule Network for Hierarchical Multi-Label Image Classification [2.507647327384289]
  Hierarchical multi-label classification applies when a multi-class image classification problem is arranged into smaller ones based upon a hierarchy or taxonomy. We propose a multi-label capsule network (ML-CapsNet) for hierarchical classification.
  arXiv  Detail & Related papers  (2022-09-13T04:17:08Z)
• Weakly-supervised Action Localization via Hierarchical Mining [76.00021423700497]
  Weakly-supervised action localization aims to localize and classify action instances in the given videos temporally with only video-level categorical labels. We propose a hierarchical mining strategy under video-level and snippet-level manners, i.e., hierarchical supervision and hierarchical consistency mining. We show that HiM-Net outperforms existing methods on THUMOS14 and ActivityNet1.3 datasets with large margins by hierarchically mining the supervision and consistency.
  arXiv  Detail & Related papers  (2022-06-22T12:19:09Z)
• Deep Hierarchical Semantic Segmentation [76.40565872257709]
  hierarchical semantic segmentation (HSS) aims at structured, pixel-wise description of visual observation in terms of a class hierarchy. HSSN casts HSS as a pixel-wise multi-label classification task, only bringing minimal architecture change to current segmentation models. With hierarchy-induced margin constraints, HSSN reshapes the pixel embedding space, so as to generate well-structured pixel representations.
  arXiv  Detail & Related papers  (2022-03-27T15:47:44Z)
• Label Relation Graphs Enhanced Hierarchical Residual Network for Hierarchical Multi-Granularity Classification [10.449261628173229]
  We study the HMC problem in which objects are labeled at any level of the hierarchy. We propose a hierarchical residual network (HRN) in which residual connections are added to features of children levels.
  arXiv  Detail & Related papers  (2022-01-10T07:17:24Z)
• Label Hierarchy Transition: Delving into Class Hierarchies to Enhance Deep Classifiers [40.993137740456014]
  We propose a unified probabilistic framework based on deep learning to address the challenges of hierarchical classification. The proposed framework can be readily adapted to any existing deep network with only minor modifications. We extend our proposed LHT framework to the skin lesion diagnosis task and validate its great potential in computer-aided diagnosis.
  arXiv  Detail & Related papers  (2021-12-04T14:58:36Z)
• Learning Hierarchical Graph Neural Networks for Image Clustering [81.5841862489509]
  We propose a hierarchical graph neural network (GNN) model that learns how to cluster a set of images into an unknown number of identities. Our hierarchical GNN uses a novel approach to merge connected components predicted at each level of the hierarchy to form a new graph at the next level.
  arXiv  Detail & Related papers  (2021-07-03T01:28:42Z)
• An evidential classifier based on Dempster-Shafer theory and deep learning [6.230751621285322]
  We propose a new classification system based on Dempster-Shafer (DS) theory and a convolutional neural network (CNN) architecture for set-valued classification. Experiments on image recognition, signal processing, and semantic-relationship classification tasks demonstrate that the proposed combination of deep CNN, DS layer, and expected utility layer makes it possible to improve classification accuracy.
  arXiv  Detail & Related papers  (2021-03-25T01:29:05Z)
• Fine-Grained Visual Classification with Efficient End-to-end Localization [49.9887676289364]
  We present an efficient localization module that can be fused with a classification network in an end-to-end setup. We evaluate the new model on the three benchmark datasets CUB200-2011, Stanford Cars and FGVC-Aircraft.
  arXiv  Detail & Related papers  (2020-05-11T14:07:06Z)
• Self-Supervised Tuning for Few-Shot Segmentation [82.32143982269892]
  Few-shot segmentation aims at assigning a category label to each image pixel with few annotated samples. Existing meta-learning method tends to fail in generating category-specifically discriminative descriptor when the visual features extracted from support images are marginalized in embedding space. This paper presents an adaptive framework tuning, in which the distribution of latent features across different episodes is dynamically adjusted based on a self-segmentation scheme.
  arXiv  Detail & Related papers  (2020-04-12T03:53:53Z)
• Hierarchical Image Classification using Entailment Cone Embeddings [68.82490011036263]
  We first inject label-hierarchy knowledge into an arbitrary CNN-based classifier. We empirically show that availability of such external semantic information in conjunction with the visual semantics from images boosts overall performance.
  arXiv  Detail & Related papers  (2020-04-02T10:22:02Z)

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.