Consistency and Diversity induced Human Motion Segmentation

• URL: http://arxiv.org/abs/2202.04861v1
• Date: Thu, 10 Feb 2022 06:23:56 GMT
• Title: Consistency and Diversity induced Human Motion Segmentation
• Authors: Tao Zhou, Huazhu Fu, Chen Gong, Ling Shao, Fatih Porikli, Haibin Ling, Jianbing Shen
• Abstract summary: We propose a novel Consistency and Diversity induced human Motion (CDMS) algorithm. Our model factorizes the source and target data into distinct multi-layer feature spaces. A multi-mutual learning strategy is carried out to reduce the domain gap between the source and target data.
• Score: 231.36289425663702
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Subspace clustering is a classical technique that has been widely used for human motion segmentation and other related tasks. However, existing segmentation methods often cluster data without guidance from prior knowledge, resulting in unsatisfactory segmentation results. To this end, we propose a novel Consistency and Diversity induced human Motion Segmentation (CDMS) algorithm. Specifically, our model factorizes the source and target data into distinct multi-layer feature spaces, in which transfer subspace learning is conducted on different layers to capture multi-level information. A multi-mutual consistency learning strategy is carried out to reduce the domain gap between the source and target data. In this way, the domain-specific knowledge and domain-invariant properties can be explored simultaneously. Besides, a novel constraint based on the Hilbert Schmidt Independence Criterion (HSIC) is introduced to ensure the diversity of multi-level subspace representations, which enables the complementarity of multi-level representations to be explored to boost the transfer learning performance. Moreover, to preserve the temporal correlations, an enhanced graph regularizer is imposed on the learned representation coefficients and the multi-level representations of the source data. The proposed model can be efficiently solved using the Alternating Direction Method of Multipliers (ADMM) algorithm. Extensive experimental results on public human motion datasets demonstrate the effectiveness of our method against several state-of-the-art approaches.

Related papers

• Improving Intrusion Detection with Domain-Invariant Representation Learning in Latent Space [4.871119861180455]
  We introduce a two-phase representation learning technique using multi-task learning. We disentangle the latent space by minimizing the mutual information between the prior and latent space. We assess the model's efficacy across multiple cybersecurity datasets.
  arXiv  Detail & Related papers  (2023-12-28T17:24:13Z)
• Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning [53.00683059396803]
  Mask image model (MIM) has been widely used due to its simplicity and effectiveness in recovering original information from masked images. We propose a decision-based MIM that utilizes reinforcement learning (RL) to automatically search for optimal image masking ratio and masking strategy. Our approach has a significant advantage over alternative self-supervised methods on the task of neuron segmentation.
  arXiv  Detail & Related papers  (2023-10-06T10:40:46Z)
• Improving Anomaly Segmentation with Multi-Granularity Cross-Domain Alignment [17.086123737443714]
  Anomaly segmentation plays a pivotal role in identifying atypical objects in images, crucial for hazard detection in autonomous driving systems. While existing methods demonstrate noteworthy results on synthetic data, they often fail to consider the disparity between synthetic and real-world data domains. We introduce the Multi-Granularity Cross-Domain Alignment framework, tailored to harmonize features across domains at both the scene and individual sample levels.
  arXiv  Detail & Related papers  (2023-08-16T22:54:49Z)
• A Novel Cross-Perturbation for Single Domain Generalization [54.612933105967606]
  Single domain generalization aims to enhance the ability of the model to generalize to unknown domains when trained on a single source domain. The limited diversity in the training data hampers the learning of domain-invariant features, resulting in compromised generalization performance. We propose CPerb, a simple yet effective cross-perturbation method to enhance the diversity of the training data.
  arXiv  Detail & Related papers  (2023-08-02T03:16:12Z)
• Generalized Semantic Segmentation by Self-Supervised Source Domain Projection and Multi-Level Contrastive Learning [79.0660895390689]
  Deep networks trained on the source domain show degraded performance when tested on unseen target domain data. We propose a Domain Projection and Contrastive Learning (DPCL) approach for generalized semantic segmentation.
  arXiv  Detail & Related papers  (2023-03-03T13:07:14Z)
• Multimodal hierarchical Variational AutoEncoders with Factor Analysis latent space [45.418113011182186]
  This study proposes a novel method to address limitations by combining Variational AutoEncoders (VAEs) with a Factor Analysis latent space (FA-VAE) The proposed FA-VAE method employs multiple VAEs to learn a private representation for each heterogeneous data view in a continuous latent space.
  arXiv  Detail & Related papers  (2022-07-19T10:46:02Z)
• A Novel Mix-normalization Method for Generalizable Multi-source Person Re-identification [49.548815417844786]
  Person re-identification (Re-ID) has achieved great success in the supervised scenario. It is difficult to directly transfer the supervised model to arbitrary unseen domains due to the model overfitting to the seen source domains. We propose MixNorm, which consists of domain-aware mix-normalization (DMN) and domain-ware center regularization (DCR)
  arXiv  Detail & Related papers  (2022-01-24T18:09:38Z)
• Towards Adaptive Semantic Segmentation by Progressive Feature Refinement [16.40758125170239]
  We propose an innovative progressive feature refinement framework, along with domain adversarial learning to boost the transferability of segmentation networks. As a result, the segmentation models trained with source domain images can be transferred to a target domain without significant performance degradation.
  arXiv  Detail & Related papers  (2020-09-30T04:17:48Z)
• Adaptively-Accumulated Knowledge Transfer for Partial Domain Adaptation [66.74638960925854]
  Partial domain adaptation (PDA) deals with a realistic and challenging problem when the source domain label space substitutes the target domain. We propose an Adaptively-Accumulated Knowledge Transfer framework (A$2$KT) to align the relevant categories across two domains.
  arXiv  Detail & Related papers  (2020-08-27T00:53:43Z)

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.