GCNet: Probing Self-Similarity Learning for Generalized Counting Network

• URL: http://arxiv.org/abs/2302.05132v1
• Date: Fri, 10 Feb 2023 09:31:37 GMT
• Title: GCNet: Probing Self-Similarity Learning for Generalized Counting Network
• Authors: Mingjie Wang and Yande Li and Jun Zhou and Graham W. Taylor and Minglun Gong
• Abstract summary: Generalized Counting Network (GCNet) is developed to recognize adaptive exemplars within the whole images. GCNet is capable of adaptively capturing them through a carefully-designed self-similarity learning strategy. It performs on par with existing exemplar-dependent methods and shows stunning cross-dataset generality on crowd-specific datasets.
• Score: 24.09746233447471
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The class-agnostic counting (CAC) problem has caught increasing attention recently due to its wide societal applications and arduous challenges. To count objects of different categories, existing approaches rely on user-provided exemplars, which is hard-to-obtain and limits their generality. In this paper, we aim to empower the framework to recognize adaptive exemplars within the whole images. A zero-shot Generalized Counting Network (GCNet) is developed, which uses a pseudo-Siamese structure to automatically and effectively learn pseudo exemplar clues from inherent repetition patterns. In addition, a weakly-supervised scheme is presented to reduce the burden of laborious density maps required by all contemporary CAC models, allowing GCNet to be trained using count-level supervisory signals in an end-to-end manner. Without providing any spatial location hints, GCNet is capable of adaptively capturing them through a carefully-designed self-similarity learning strategy. Extensive experiments and ablation studies on the prevailing benchmark FSC147 for zero-shot CAC demonstrate the superiority of our GCNet. It performs on par with existing exemplar-dependent methods and shows stunning cross-dataset generality on crowd-specific datasets, e.g., ShanghaiTech Part A, Part B and UCF_QNRF.

Related papers

• A Recipe for CAC: Mosaic-based Generalized Loss for Improved Class-Agnostic Counting [27.439965991083177]
  Class counting (CAC) is a vision computation task that can be used to count the total occurrence number of any given reference objects in the query image. Given a multi-class setting, models don't consider reference images and instead blindly match all dominant objects in the query image. We introduce a new evaluation protocol and metrics for resolving the problem behind the existing CAC evaluation scheme.
  arXiv  Detail & Related papers  (2024-04-15T14:23:39Z)
• CDIMC-net: Cognitive Deep Incomplete Multi-view Clustering Network [53.72046586512026]
  We propose a novel incomplete multi-view clustering network, called Cognitive Deep Incomplete Multi-view Clustering Network (CDIMC-net) It captures the high-level features and local structure of each view by incorporating the view-specific deep encoders and graph embedding strategy into a framework. Based on the human cognition, i.e., learning from easy to hard, it introduces a self-paced strategy to select the most confident samples for model training.
  arXiv  Detail & Related papers  (2024-03-28T15:45:03Z)
• GACL: Exemplar-Free Generalized Analytic Continual Learning [20.49481895737308]
  Class incremental learning (CIL) trains a network on sequential tasks with separated categories in each task but suffers from catastrophic forgetting. The generalized CIL (GCIL) aims to address the CIL problem in a more real-world scenario, where incoming data have mixed data categories and unknown sample size distribution. We propose a new exemplar-free GCIL technique named generalized analytic continual learning (GACL)
  arXiv  Detail & Related papers  (2024-03-23T03:56:31Z)
• SQLNet: Scale-Modulated Query and Localization Network for Few-Shot Class-Agnostic Counting [71.38754976584009]
  The class-agnostic counting (CAC) task has recently been proposed to solve the problem of counting all objects of an arbitrary class with several exemplars given in the input image. We propose a novel localization-based CAC approach, termed Scale-modulated Query and Localization Network (Net) It fully explores the scales of exemplars in both the query and localization stages and achieves effective counting by accurately locating each object and predicting its approximate size.
  arXiv  Detail & Related papers  (2023-11-16T16:50:56Z)
• Consistency-Regularized Region-Growing Network for Semantic Segmentation of Urban Scenes with Point-Level Annotations [17.13291434132985]
  We propose a consistency-regularized region-growing network (CRGNet) to reduce the annotation burden. CRGNet iteratively selects unlabeled pixels with high confidence to expand the annotated area from the original sparse points. We find such a simple regularization strategy is yet very useful to control the quality of the region-growing mechanism.
  arXiv  Detail & Related papers  (2022-02-08T09:27:01Z)
• Self-Ensembling GAN for Cross-Domain Semantic Segmentation [107.27377745720243]
  This paper proposes a self-ensembling generative adversarial network (SE-GAN) exploiting cross-domain data for semantic segmentation. In SE-GAN, a teacher network and a student network constitute a self-ensembling model for generating semantic segmentation maps, which together with a discriminator, forms a GAN. Despite its simplicity, we find SE-GAN can significantly boost the performance of adversarial training and enhance the stability of the model.
  arXiv  Detail & Related papers  (2021-12-15T09:50:25Z)
• Calibrating Class Activation Maps for Long-Tailed Visual Recognition [60.77124328049557]
  We present two effective modifications of CNNs to improve network learning from long-tailed distribution. First, we present a Class Activation Map (CAMC) module to improve the learning and prediction of network classifiers. Second, we investigate the use of normalized classifiers for representation learning in long-tailed problems.
  arXiv  Detail & Related papers  (2021-08-29T05:45:03Z)
• Graph Prototypical Networks for Few-shot Learning on Attributed Networks [72.31180045017835]
  We propose a graph meta-learning framework -- Graph Prototypical Networks (GPN) GPN is able to perform textitmeta-learning on an attributed network and derive a highly generalizable model for handling the target classification task.
  arXiv  Detail & Related papers  (2020-06-23T04:13:23Z)
• Learning to Cluster Faces via Confidence and Connectivity Estimation [136.5291151775236]
  We propose a fully learnable clustering framework without requiring a large number of overlapped subgraphs. Our method significantly improves clustering accuracy and thus performance of the recognition models trained on top, yet it is an order of magnitude more efficient than existing supervised methods.
  arXiv  Detail & Related papers  (2020-04-01T13:39:37Z)

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.