Meta-Decomposition: Dynamic Segmentation Approach Selection in IoT-based Activity Recognition

• URL: http://arxiv.org/abs/2404.11742v1
• Date: Wed, 17 Apr 2024 20:50:28 GMT
• Title: Meta-Decomposition: Dynamic Segmentation Approach Selection in IoT-based Activity Recognition
• Authors: Seyed M. R. Modaresi, Aomar Osmani, Mohammadreza Razzazi, Abdelghani Chibani,
• Abstract summary: Internet of Things (IoT) devices generate heterogeneous data over time. Many IoT applications, including IoT-based activity recognition, aim to address the limitations of individual events and streamline the process. However, this step introduces at least two families of uncontrollable biases. We propose to redefine the segmentation problem as a special case of a decomposition problem, including three key components: a decomposer, resolutions, and a composer.
• Score: 6.887244952811574
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Internet of Things (IoT) devices generate heterogeneous data over time; and relying solely on individual data points is inadequate for accurate analysis. Segmentation is a common preprocessing step in many IoT applications, including IoT-based activity recognition, aiming to address the limitations of individual events and streamline the process. However, this step introduces at least two families of uncontrollable biases. The first is caused by the changes made by the segmentation process on the initial problem space, such as dividing the input data into 60 seconds windows. The second category of biases results from the segmentation process itself, including the fixation of the segmentation method and its parameters. To address these biases, we propose to redefine the segmentation problem as a special case of a decomposition problem, including three key components: a decomposer, resolutions, and a composer. The inclusion of the composer task in the segmentation process facilitates an assessment of the relationship between the original problem and the problem after the segmentation. Therefore, It leads to an improvement in the evaluation process and, consequently, in the selection of the appropriate segmentation method. Then, we formally introduce our novel meta-decomposition or learning-to-decompose approach. It reduces the segmentation biases by considering the segmentation as a hyperparameter to be optimized by the outer learning problem. Therefore, meta-decomposition improves the overall system performance by dynamically selecting the appropriate segmentation method without including the mentioned biases. Extensive experiments on four real-world datasets demonstrate the effectiveness of our proposal.

Related papers

• A Bottom-Up Approach to Class-Agnostic Image Segmentation [4.086366531569003]
  We present a novel bottom-up formulation for addressing the class-agnostic segmentation problem. We supervise our network directly on the projective sphere of its feature space. Our bottom-up formulation exhibits exceptional generalization capability, even when trained on datasets designed for class-based segmentation.
  arXiv  Detail & Related papers  (2024-09-20T17:56:02Z)
• Embodied Uncertainty-Aware Object Segmentation [38.52448300879023]
  We introduce uncertainty-aware object instance segmentation (UncOS) and demonstrate its usefulness for embodied interactive segmentation. We obtain a set of region-factored segmentation hypotheses together with confidence estimates by making multiple queries of large pre-trained models. The output can also serve as input to a belief-driven process for selecting robot actions to perturb the scene to reduce ambiguity.
  arXiv  Detail & Related papers  (2024-08-08T21:29:22Z)
• Appearance-Based Refinement for Object-Centric Motion Segmentation [85.2426540999329]
  We introduce an appearance-based refinement method that leverages temporal consistency in video streams to correct inaccurate flow-based proposals. Our approach involves a sequence-level selection mechanism that identifies accurate flow-predicted masks as exemplars. Its performance is evaluated on multiple video segmentation benchmarks, including DAVIS, YouTube, SegTrackv2, and FBMS-59.
  arXiv  Detail & Related papers  (2023-12-18T18:59:51Z)
• Multi-body SE(3) Equivariance for Unsupervised Rigid Segmentation and Motion Estimation [49.56131393810713]
  We present an SE(3) equivariant architecture and a training strategy to tackle this task in an unsupervised manner. Our method excels in both model performance and computational efficiency, with only 0.25M parameters and 0.92G FLOPs.
  arXiv  Detail & Related papers  (2023-06-08T22:55:32Z)
• Exploring Cycle Consistency Learning in Interactive Volume Segmentation [39.000842538436714]
  We interactively approach medical volume segmentation via two decoupled modules: interaction-to-segmentation and segmentation propagation. We propose a simple yet effective cycle consistency loss that regularizes an intermediate segmentation by referencing the accurate segmentation in the starting slice. With cycle consistency training, the propagation network is better regularized than in standard forward-only training approaches.
  arXiv  Detail & Related papers  (2023-03-11T20:25:05Z)
• Temporal Segment Transformer for Action Segmentation [54.25103250496069]
  We propose an attention based approach which we call textittemporal segment transformer, for joint segment relation modeling and denoising. The main idea is to denoise segment representations using attention between segment and frame representations, and also use inter-segment attention to capture temporal correlations between segments. We show that this novel architecture achieves state-of-the-art accuracy on the popular 50Salads, GTEA and Breakfast benchmarks.
  arXiv  Detail & Related papers  (2023-02-25T13:05:57Z)
• A Survey on Label-efficient Deep Segmentation: Bridging the Gap between Weak Supervision and Dense Prediction [115.9169213834476]
  This paper offers a comprehensive review on label-efficient segmentation methods. We first develop a taxonomy to organize these methods according to the supervision provided by different types of weak labels. Next, we summarize the existing label-efficient segmentation methods from a unified perspective.
  arXiv  Detail & Related papers  (2022-07-04T06:21:01Z)
• SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation [111.61261419566908]
  Deep neural networks (DNNs) are usually trained on a closed set of semantic classes. They are ill-equipped to handle previously-unseen objects. detecting and localizing such objects is crucial for safety-critical applications such as perception for automated driving.
  arXiv  Detail & Related papers  (2021-04-30T07:58:19Z)
• Target-Aware Object Discovery and Association for Unsupervised Video Multi-Object Segmentation [79.6596425920849]
  This paper addresses the task of unsupervised video multi-object segmentation. We introduce a novel approach for more accurate and efficient unseen-temporal segmentation. We evaluate the proposed approach on DAVIS$_17$ and YouTube-VIS, and the results demonstrate that it outperforms state-of-the-art methods both in segmentation accuracy and inference speed.
  arXiv  Detail & Related papers  (2021-04-10T14:39:44Z)
• Deep Variational Instance Segmentation [7.334808870313923]
  State-of-the-art algorithms often employ two separate stages, the first one generating object proposals and the second one recognizing and refining the boundaries. We propose a novel algorithm that directly utilizes a fully convolutional network (FCN) to predict instance labels.
  arXiv  Detail & Related papers  (2020-07-22T17:57:49Z)

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.