Bringing motion taxonomies to continuous domains via GPLVM on hyperbolic manifolds

• URL: http://arxiv.org/abs/2210.01672v5
• Date: Sun, 15 Sep 2024 14:40:59 GMT
• Title: Bringing motion taxonomies to continuous domains via GPLVM on hyperbolic manifolds
• Authors: Noémie Jaquier, Leonel Rozo, Miguel González-Duque, Viacheslav Borovitskiy, Tamim Asfour,
• Abstract summary: Human motion serves as high-level hierarchical abstractions that classify how humans move and interact with their environment. We propose to model taxonomy data via hyperbolic embeddings that capture the associated hierarchical structure. We show that our model properly encodes unseen data from existing or new taxonomy categories, and outperforms its Euclidean and VAE-based counterparts.
• Score: 8.385386712928785
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human motion taxonomies serve as high-level hierarchical abstractions that classify how humans move and interact with their environment. They have proven useful to analyse grasps, manipulation skills, and whole-body support poses. Despite substantial efforts devoted to design their hierarchy and underlying categories, their use remains limited. This may be attributed to the lack of computational models that fill the gap between the discrete hierarchical structure of the taxonomy and the high-dimensional heterogeneous data associated to its categories. To overcome this problem, we propose to model taxonomy data via hyperbolic embeddings that capture the associated hierarchical structure. We achieve this by formulating a novel Gaussian process hyperbolic latent variable model that incorporates the taxonomy structure through graph-based priors on the latent space and distance-preserving back constraints. We validate our model on three different human motion taxonomies to learn hyperbolic embeddings that faithfully preserve the original graph structure. We show that our model properly encodes unseen data from existing or new taxonomy categories, and outperforms its Euclidean and VAE-based counterparts. Finally, through proof-of-concept experiments, we show that our model may be used to generate realistic trajectories between the learned embeddings.

Related papers

• From Semantics to Hierarchy: A Hybrid Euclidean-Tangent-Hyperbolic Space Model for Temporal Knowledge Graph Reasoning [1.1372536310854844]
  Temporal knowledge graph (TKG) reasoning predicts future events based on historical data. Existing Euclidean models excel at capturing semantics but struggle with hierarchy. We propose a novel hybrid geometric space approach that leverages the strengths of both Euclidean and hyperbolic models.
  arXiv  Detail & Related papers  (2024-08-30T10:33:08Z)
• Hierarchical Blockmodelling for Knowledge Graphs [0.5530212768657544]
  We use blockmodels for the purpose of hierarchical entity clustering on knowledge graphs. The integration of the Nested Chinese Restaurant Process and the Stick Breaking Process into the generative model allows for the induction of hierarchical clusterings. We evaluate our model on synthetic and real-world datasets and quantitatively compare against benchmark models.
  arXiv  Detail & Related papers  (2024-08-28T09:04:15Z)
• Data-efficient Large Vision Models through Sequential Autoregression [58.26179273091461]
  We develop an efficient, autoregression-based vision model on a limited dataset. We demonstrate how this model achieves proficiency in a spectrum of visual tasks spanning both high-level and low-level semantic understanding. Our empirical evaluations underscore the model's agility in adapting to various tasks, heralding a significant reduction in the parameter footprint.
  arXiv  Detail & Related papers  (2024-02-07T13:41:53Z)
• MoDi: Unconditional Motion Synthesis from Diverse Data [51.676055380546494]
  We present MoDi, an unconditional generative model that synthesizes diverse motions. Our model is trained in a completely unsupervised setting from a diverse, unstructured and unlabeled motion dataset. We show that despite the lack of any structure in the dataset, the latent space can be semantically clustered.
  arXiv  Detail & Related papers  (2022-06-16T09:06:25Z)
• Hierarchical Graph-Convolutional Variational AutoEncoding for Generative Modelling of Human Motion [1.2599533416395767]
  Models of human motion commonly focus either on trajectory prediction or action classification but rarely both. Here we propose a novel architecture based on hierarchical variational autoencoders and deep graph convolutional neural networks for generating a holistic model of action over multiple time-scales. We show this Hierarchical Graph-conational Varivolutional Autoencoder (HG-VAE) to be capable of generating coherent actions, detecting out-of-distribution data, and imputing missing data by gradient ascent on the model's posterior.
  arXiv  Detail & Related papers  (2021-11-24T16:21:07Z)
• Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones [64.75766944882389]
  We present ConE (Cone Embedding), a KG embedding model that is able to simultaneously model multiple hierarchical as well as non-hierarchical relations in a knowledge graph. In particular, ConE uses cone containment constraints in different subspaces of the hyperbolic embedding space to capture multiple heterogeneous hierarchies. Our approach yields new state-of-the-art Hits@1 of 45.3% on WN18RR and 16.1% on DDB14 (0.231 MRR)
  arXiv  Detail & Related papers  (2021-10-28T07:16:08Z)
• TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning [87.38675639186405]
  We propose a novel graph neural network approach, called TCL, which deals with the dynamically-evolving graph in a continuous-time fashion. To the best of our knowledge, this is the first attempt to apply contrastive learning to representation learning on dynamic graphs.
  arXiv  Detail & Related papers  (2021-05-17T15:33:25Z)
• Hyperbolic Graph Embedding with Enhanced Semi-Implicit Variational Inference [48.63194907060615]
  We build off of semi-implicit graph variational auto-encoders to capture higher-order statistics in a low-dimensional graph latent representation. We incorporate hyperbolic geometry in the latent space through a Poincare embedding to efficiently represent graphs exhibiting hierarchical structure.
  arXiv  Detail & Related papers  (2020-10-31T05:48:34Z)
• A Fully Hyperbolic Neural Model for Hierarchical Multi-Class Classification [7.8176853587105075]
  Hyperbolic spaces offer a mathematically appealing approach for learning hierarchical representations of symbolic data. This work proposes a fully hyperbolic model for multi-class multi-label classification, which performs all operations in hyperbolic space. A thorough analysis sheds light on the impact of each component in the final prediction and showcases its ease of integration with Euclidean layers.
  arXiv  Detail & Related papers  (2020-10-05T14:42:56Z)
• TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network [62.12557274257303]
  Taxonomies consist of machine-interpretable semantics and provide valuable knowledge for many web applications. We propose a novel self-supervised framework, named TaxoExpan, which automatically generates a set of query concept, anchor concept> pairs from the existing taxonomy as training data. We develop two innovative techniques in TaxoExpan: (1) a position-enhanced graph neural network that encodes the local structure of an anchor concept in the existing taxonomy, and (2) a noise-robust training objective that enables the learned model to be insensitive to the label noise in the self-supervision data.
  arXiv  Detail & Related papers  (2020-01-26T21:30:21Z)

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.