Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation

• URL: http://arxiv.org/abs/2108.02980v2
• Date: Fri, 31 Mar 2023 12:02:10 GMT
• Title: Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation
• Authors: Yongtuo Liu, Dan Xu, Sucheng Ren, Hanjie Wu, Hongmin Cai, Shengfeng He
• Abstract summary: We propose to untangle emphdomain-invariant crowd and emphdomain-specific background from crowd images. Specifically, to disentangle crowd from background, we propose to learn crowd segmentation from point-level crowd counting annotations. Based on the derived segmentation, we design a crowd-aware domain adaptation mechanism consisting of two crowd-aware adaptation modules.
• Score: 40.17242574440061
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Due to domain shift, a large performance drop is usually observed when a trained crowd counting model is deployed in the wild. While existing domain-adaptive crowd counting methods achieve promising results, they typically regard each crowd image as a whole and reduce domain discrepancies in a holistic manner, thus limiting further improvement of domain adaptation performance. To this end, we propose to untangle \emph{domain-invariant} crowd and \emph{domain-specific} background from crowd images and design a fine-grained domain adaption method for crowd counting. Specifically, to disentangle crowd from background, we propose to learn crowd segmentation from point-level crowd counting annotations in a weakly-supervised manner. Based on the derived segmentation, we design a crowd-aware domain adaptation mechanism consisting of two crowd-aware adaptation modules, i.e., Crowd Region Transfer (CRT) and Crowd Density Alignment (CDA). The CRT module is designed to guide crowd features transfer across domains beyond background distractions. The CDA module dedicates to regularising target-domain crowd density generation by its own crowd density distribution. Our method outperforms previous approaches consistently in the widely-used adaptation scenarios.

Related papers

• Dynamic Proxy Domain Generalizes the Crowd Localization by Better Binary Segmentation [65.13362950067744]
  Crowd localization targets on predicting each instance precise location within an image. Current advanced methods propose the pixel-wise binary classification to tackle the congested prediction. We propose a Dynamic Proxy Domain (DPD) method to generalize the learner under domain shift.
  arXiv  Detail & Related papers  (2024-04-22T08:58:57Z)
• Robust Zero-Shot Crowd Counting and Localization With Adaptive Resolution SAM [55.93697196726016]
  We propose a simple yet effective crowd counting method by utilizing the Segment-Everything-Everywhere Model (SEEM) We show that SEEM's performance in dense crowd scenes is limited, primarily due to the omission of many persons in high-density areas. Our proposed method achieves the best unsupervised performance in crowd counting, while also being comparable to some supervised methods.
  arXiv  Detail & Related papers  (2024-02-27T13:55:17Z)
• Fine-Grained Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation [3.0081059328558624]
  We focus on introducing a fine-grained unsupervised framework for domain adaptation. We propose to use a vector to control the generator to synthesize a fake image with given features. And then, we can apply various augmentations to the dataset by searching the feature dictionary.
  arXiv  Detail & Related papers  (2023-11-25T18:08:59Z)
• Domain-General Crowd Counting in Unseen Scenarios [25.171343652312974]
  Domain shift across crowd data severely hinders crowd counting models to generalize to unseen scenarios. We introduce a dynamic sub-domain division scheme which divides the source domain into multiple sub-domains. In order to disentangle domain-invariant information from domain-specific information in image features, we design the domain-invariant and -specific crowd memory modules.
  arXiv  Detail & Related papers  (2022-12-05T19:52:28Z)
• Distribution Regularized Self-Supervised Learning for Domain Adaptation of Semantic Segmentation [3.284878354988896]
  This paper proposes a pixel-level distribution regularization scheme (DRSL) for self-supervised domain adaptation of semantic segmentation. In a typical setting, the classification loss forces the semantic segmentation model to greedily learn the representations that capture inter-class variations. We capture pixel-level intra-class variations through class-aware multi-modal distribution learning.
  arXiv  Detail & Related papers  (2022-06-20T09:52:49Z)
• Scene-Adaptive Attention Network for Crowd Counting [31.29858034122248]
  This paper proposes a scene-adaptive attention network, termed SAANet. We design a deformable attention in-built Transformer backbone, which learns adaptive feature representations with deformable sampling locations and dynamic attention weights. We conduct extensive experiments on four challenging crowd counting benchmarks, demonstrating that our method achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-12-31T15:03:17Z)
• Coarse to Fine: Domain Adaptive Crowd Counting via Adversarial Scoring Network [58.05473757538834]
  This paper proposes a novel adversarial scoring network (ASNet) to bridge the gap across domains from coarse to fine granularity. Three sets of migration experiments show that the proposed methods achieve state-of-the-art counting performance.
  arXiv  Detail & Related papers  (2021-07-27T14:47:24Z)
• Instance Level Affinity-Based Transfer for Unsupervised Domain Adaptation [74.71931918541748]
  We propose an instance affinity based criterion for source to target transfer during adaptation, called ILA-DA. We first propose a reliable and efficient method to extract similar and dissimilar samples across source and target, and utilize a multi-sample contrastive loss to drive the domain alignment process. We verify the effectiveness of ILA-DA by observing consistent improvements in accuracy over popular domain adaptation approaches on a variety of benchmark datasets.
  arXiv  Detail & Related papers  (2021-04-03T01:33:14Z)
• Cross-Domain Grouping and Alignment for Domain Adaptive Semantic Segmentation [74.3349233035632]
  Existing techniques to adapt semantic segmentation networks across the source and target domains within deep convolutional neural networks (CNNs) do not consider an inter-class variation within the target domain itself or estimated category. We introduce a learnable clustering module, and a novel domain adaptation framework called cross-domain grouping and alignment. Our method consistently boosts the adaptation performance in semantic segmentation, outperforming the state-of-the-arts on various domain adaptation settings.
  arXiv  Detail & Related papers  (2020-12-15T11:36:21Z)

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.