ActiLabel: A Combinatorial Transfer Learning Framework for Activity Recognition

• URL: http://arxiv.org/abs/2003.07415v1
• Date: Mon, 16 Mar 2020 19:19:08 GMT
• Title: ActiLabel: A Combinatorial Transfer Learning Framework for Activity Recognition
• Authors: Parastoo Alinia, Iman Mirzadeh, and Hassan Ghasemzadeh
• Abstract summary: ActiLabel is a framework that learns structural similarities among events in an arbitrary domain and those of a different domain. Experiments based on three public datasets demonstrate the superiority of ActiLabel over state-of-the-art transfer learning and deep learning methods.
• Score: 14.605223647792862
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sensor-based human activity recognition has become a critical component of many emerging applications ranging from behavioral medicine to gaming. However, an unprecedented increase in the diversity of sensor devices in the Internet-of-Things era has limited the adoption of activity recognition models for use across different domains. We propose ActiLabel a combinatorial framework that learns structural similarities among the events in an arbitrary domain and those of a different domain. The structural similarities are captured through a graph model, referred to as the it dependency graph, which abstracts details of activity patterns in low-level signal and feature space. The activity labels are then autonomously learned by finding an optimal tiered mapping between the dependency graphs. Extensive experiments based on three public datasets demonstrate the superiority of ActiLabel over state-of-the-art transfer learning and deep learning methods.

Related papers

• Cross-Domain HAR: Few Shot Transfer Learning for Human Activity Recognition [0.2944538605197902]
  We present an approach for economic use of publicly available labeled HAR datasets for effective transfer learning. We introduce a novel transfer learning framework, Cross-Domain HAR, which follows the teacher-student self-training paradigm. We demonstrate the effectiveness of our approach for practically relevant few shot activity recognition scenarios.
  arXiv  Detail & Related papers  (2023-10-22T19:13:25Z)
• Unleashing the Power of Shared Label Structures for Human Activity Recognition [36.66107380956779]
  We propose SHARE, a framework that takes into account shared structures of label names for different activities. To exploit the shared structures, SHARE comprises an encoder for extracting features from input sensory time series and a decoder for generating label names as a token sequence. We also propose three label augmentation techniques to help the model more effectively capture semantic structures across activities.
  arXiv  Detail & Related papers  (2023-01-01T22:50:08Z)
• Semantic Representation and Dependency Learning for Multi-Label Image Recognition [76.52120002993728]
  We propose a novel and effective semantic representation and dependency learning (SRDL) framework to learn category-specific semantic representation for each category. Specifically, we design a category-specific attentional regions (CAR) module to generate channel/spatial-wise attention matrices to guide model. We also design an object erasing (OE) module to implicitly learn semantic dependency among categories by erasing semantic-aware regions.
  arXiv  Detail & Related papers  (2022-04-08T00:55:15Z)
• An Exploration of Active Learning for Affective Digital Phenotyping [4.790279027864381]
  Active learning is a paradigm for using algorithms to computationally select a useful subset of data points to label. We explore active learning for naturalistic computer vision emotion data, a particularly heterogeneous and complex data space. We find that active learning using information generated during gameplay slightly outperforms random selection of the same number of labeled frames.
  arXiv  Detail & Related papers  (2022-04-05T01:01:32Z)
• SATS: Self-Attention Transfer for Continual Semantic Segmentation [50.51525791240729]
  continual semantic segmentation suffers from the same catastrophic forgetting issue as in continual classification learning. This study proposes to transfer a new type of information relevant to knowledge, i.e. the relationships between elements within each image. The relationship information can be effectively obtained from the self-attention maps in a Transformer-style segmentation model.
  arXiv  Detail & Related papers  (2022-03-15T06:09:28Z)
• Multi-Label Image Classification with Contrastive Learning [57.47567461616912]
  We show that a direct application of contrastive learning can hardly improve in multi-label cases. We propose a novel framework for multi-label classification with contrastive learning in a fully supervised setting.
  arXiv  Detail & Related papers  (2021-07-24T15:00:47Z)
• Knowledge-Guided Multi-Label Few-Shot Learning for General Image Recognition [75.44233392355711]
  KGGR framework exploits prior knowledge of statistical label correlations with deep neural networks. It first builds a structured knowledge graph to correlate different labels based on statistical label co-occurrence. Then, it introduces the label semantics to guide learning semantic-specific features. It exploits a graph propagation network to explore graph node interactions.
  arXiv  Detail & Related papers  (2020-09-20T15:05:29Z)
• All About Knowledge Graphs for Actions [82.39684757372075]
  We propose a better understanding of knowledge graphs (KGs) that can be utilized for zero-shot and few-shot action recognition. We study three different construction mechanisms for KGs: action embeddings, action-object embeddings, visual embeddings. We present extensive analysis of the impact of different KGs on different experimental setups.
  arXiv  Detail & Related papers  (2020-08-28T01:44:01Z)
• FineGym: A Hierarchical Video Dataset for Fine-grained Action Understanding [118.32912239230272]
  FineGym is a new action recognition dataset built on top of gymnastic videos. It provides temporal annotations at both action and sub-action levels with a three-level semantic hierarchy. This new level of granularity presents significant challenges for action recognition.
  arXiv  Detail & Related papers  (2020-04-14T17:55:21Z)
• Sequential Weakly Labeled Multi-Activity Localization and Recognition on Wearable Sensors using Recurrent Attention Networks [13.64024154785943]
  We propose a recurrent attention network (RAN) to handle sequential weakly labeled multi-activity recognition and location tasks. Our RAN model can simultaneously infer multi-activity types from the coarse-grained sequential weak labels. It will greatly reduce the burden of manual labeling.
  arXiv  Detail & Related papers  (2020-04-13T04:57:09Z)

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.