A Generically Contrastive Spatiotemporal Representation Enhancement for 3D Skeleton Action Recognition

• URL: http://arxiv.org/abs/2312.15144v4
• Date: Mon, 17 Mar 2025 08:45:27 GMT
• Title: A Generically Contrastive Spatiotemporal Representation Enhancement for 3D Skeleton Action Recognition
• Authors: Shaojie Zhang, Jianqin Yin, Yonghao Dang,
• Abstract summary: We propose a Contrastive Spatiotemporal Representation Enhancement (CSRE) framework to obtain more discriminative representations from the sequences.<n>Specifically, we decompose the representation into spatial- and temporal-specific features to explore fine-grained motion patterns.<n>To explicitly exploit the latent data distributions, we employ the attentive features to contrastive learning, which models the cross-sequence semantic relations.
• Score: 10.403751563214113
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Skeleton-based action recognition is a central task in computer vision and human-robot interaction. However, most previous methods suffer from overlooking the explicit exploitation of the latent data distributions (i.e., the intra-class variations and inter-class relations), thereby leading to confusion about ambiguous samples and sub-optimum solutions of the skeleton encoders. To mitigate this, we propose a Contrastive Spatiotemporal Representation Enhancement (CSRE) framework to obtain more discriminative representations from the sequences, which can be incorporated into various previous skeleton encoders and can be removed when testing. Specifically, we decompose the representation into spatial- and temporal-specific features to explore fine-grained motion patterns along the corresponding dimensions. Furthermore, to explicitly exploit the latent data distributions, we employ the attentive features to contrastive learning, which models the cross-sequence semantic relations by pulling together the features from the positive pairs and pushing away the negative pairs. Extensive experiments show that CSRE with five various skeleton encoders (HCN, 2S-AGCN, CTR-GCN, Hyperformer, and BlockGCN) achieves solid improvements on five benchmarks. The code will be released at https://github.com/zhshj0110/CSRE.

Related papers

• USDRL: Unified Skeleton-Based Dense Representation Learning with Multi-Grained Feature Decorrelation [24.90512145836643]
  We introduce a Unified Skeleton-based Dense Representation Learning framework based on feature decorrelation. We show that our approach significantly outperforms the current state-of-the-art (SOTA) approaches.
  arXiv  Detail & Related papers  (2024-12-12T12:20:27Z)
• Neuron: Learning Context-Aware Evolving Representations for Zero-Shot Skeleton Action Recognition [64.56321246196859]
  We propose a novel dyNamically Evolving dUal skeleton-semantic syneRgistic framework. We first construct the spatial-temporal evolving micro-prototypes and integrate dynamic context-aware side information. We introduce the spatial compression and temporal memory mechanisms to guide the growth of spatial-temporal micro-prototypes.
  arXiv  Detail & Related papers  (2024-11-18T05:16:11Z)
• SA-DVAE: Improving Zero-Shot Skeleton-Based Action Recognition by Disentangled Variational Autoencoders [7.618223798662929]
  We propose SA-DVAE -- Semantic Alignment via Disentangled Variational Autoencoders. We implement this idea via a pair of modality-specific variational autoencoders coupled with a total correction penalty. Experiments show that SA-DAVE produces improved performance over existing methods.
  arXiv  Detail & Related papers  (2024-07-18T12:35:46Z)
• An Information Compensation Framework for Zero-Shot Skeleton-based Action Recognition [49.45660055499103]
  Zero-shot human skeleton-based action recognition aims to construct a model that can recognize actions outside the categories seen during training. Previous research has focused on aligning sequences' visual and semantic spatial distributions. We introduce a new loss function sampling method to obtain a tight and robust representation.
  arXiv  Detail & Related papers  (2024-06-02T06:53:01Z)
• S^2Former-OR: Single-Stage Bi-Modal Transformer for Scene Graph Generation in OR [50.435592120607815]
  Scene graph generation (SGG) of surgical procedures is crucial in enhancing holistically cognitive intelligence in the operating room (OR) Previous works have primarily relied on multi-stage learning, where the generated semantic scene graphs depend on intermediate processes with pose estimation and object detection. In this study, we introduce a novel single-stage bi-modal transformer framework for SGG in the OR, termed S2Former-OR.
  arXiv  Detail & Related papers  (2024-02-22T11:40:49Z)
• FOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent Space [7.324708513042455]
  This paper proposes a novel contrastive learning framework, called FOCAL, for extracting comprehensive features from multimodal time-series sensing signals. It consistently outperforms the state-of-the-art baselines in downstream tasks with a clear margin.
  arXiv  Detail & Related papers  (2023-10-30T22:55:29Z)
• Exploiting Spatial-temporal Data for Sleep Stage Classification via Hypergraph Learning [16.802013781690402]
  We propose a dynamic learning framework STHL, which introduces hypergraph to encode spatial-temporal data for sleep stage classification. Our proposed STHL outperforms the state-of-the-art models in sleep stage classification tasks.
  arXiv  Detail & Related papers  (2023-09-05T11:01:30Z)
• Linking data separation, visual separation, and classifier performance using pseudo-labeling by contrastive learning [125.99533416395765]
  We argue that the performance of the final classifier depends on the data separation present in the latent space and visual separation present in the projection. We demonstrate our results by the classification of five real-world challenging image datasets of human intestinal parasites with only 1% supervised samples.
  arXiv  Detail & Related papers  (2023-02-06T10:01:38Z)
• Spatiotemporal Decouple-and-Squeeze Contrastive Learning for Semi-Supervised Skeleton-based Action Recognition [12.601122522537459]
  We propose a novel Stemporal Decouple Contrastive Learning (SDS-CL) framework to learn more abundant representations of skeleton-based actions. We present a new Temporal-squeezing Loss (STL), a new Temporal-squeezing Loss (TSL), and the Global-contrasting Loss (GL) to contrast the spatial-squeezing joint and motion features at the frame level, temporal-squeezing joint and motion features at the joint level, as well as global joint and motion features at the skeleton level.
  arXiv  Detail & Related papers  (2023-02-05T06:52:25Z)
• Leveraging Spatio-Temporal Dependency for Skeleton-Based Action Recognition [9.999149887494646]
  Skeleton-based action recognition has attracted considerable attention due to its compact representation of the human body's skeletal sucrture. Many recent methods have achieved remarkable performance using graph convolutional networks (GCNs) and convolutional neural networks (CNNs)
  arXiv  Detail & Related papers  (2022-12-09T10:37:22Z)
• Learning Appearance-motion Normality for Video Anomaly Detection [11.658792932975652]
  We propose spatial-temporal memories augmented two-stream auto-encoder framework. It learns the appearance normality and motion normality independently and explores the correlations via adversarial learning. Our framework outperforms the state-of-the-art methods, achieving AUCs of 98.1% and 89.8% on UCSD Ped2 and CUHK Avenue datasets.
  arXiv  Detail & Related papers  (2022-07-27T08:30:19Z)
• Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition [88.34182299496074]
  Action labels are only available on a source dataset, but unavailable on a target dataset in the training stage. We utilize a self-supervision scheme to reduce the domain shift between two skeleton-based action datasets. By segmenting and permuting temporal segments or human body parts, we design two self-supervised learning classification tasks.
  arXiv  Detail & Related papers  (2022-07-17T07:05:39Z)
• Multi-scale and Cross-scale Contrastive Learning for Semantic Segmentation [5.281694565226513]
  We apply contrastive learning to enhance the discriminative power of the multi-scale features extracted by semantic segmentation networks. By first mapping the encoder's multi-scale representations to a common feature space, we instantiate a novel form of supervised local-global constraint.
  arXiv  Detail & Related papers  (2022-03-25T01:24:24Z)
• Joint-bone Fusion Graph Convolutional Network for Semi-supervised Skeleton Action Recognition [65.78703941973183]
  We propose a novel correlation-driven joint-bone fusion graph convolutional network (CD-JBF-GCN) as an encoder and use a pose prediction head as a decoder. Specifically, the CD-JBF-GC can explore the motion transmission between the joint stream and the bone stream. The pose prediction based auto-encoder in the self-supervised training stage allows the network to learn motion representation from unlabeled data.
  arXiv  Detail & Related papers  (2022-02-08T16:03:15Z)
• Spatial-Temporal Correlation and Topology Learning for Person Re-Identification in Videos [78.45050529204701]
  We propose a novel framework to pursue discriminative and robust representation by modeling cross-scale spatial-temporal correlation. CTL utilizes a CNN backbone and a key-points estimator to extract semantic local features from human body. It explores a context-reinforced topology to construct multi-scale graphs by considering both global contextual information and physical connections of human body.
  arXiv  Detail & Related papers  (2021-04-15T14:32:12Z)
• Adversarial Feature Augmentation and Normalization for Visual Recognition [109.6834687220478]
  Recent advances in computer vision take advantage of adversarial data augmentation to ameliorate the generalization ability of classification models. Here, we present an effective and efficient alternative that advocates adversarial augmentation on intermediate feature embeddings. We validate the proposed approach across diverse visual recognition tasks with representative backbone networks.
  arXiv  Detail & Related papers  (2021-03-22T20:36:34Z)
• Tensor Representations for Action Recognition [54.710267354274194]
  Human actions in sequences are characterized by the complex interplay between spatial features and their temporal dynamics. We propose novel tensor representations for capturing higher-order relationships between visual features for the task of action recognition. We use higher-order tensors and so-called Eigenvalue Power Normalization (NEP) which have been long speculated to perform spectral detection of higher-order occurrences.
  arXiv  Detail & Related papers  (2020-12-28T17:27:18Z)
• A Self-Supervised Gait Encoding Approach with Locality-Awareness for 3D Skeleton Based Person Re-Identification [65.18004601366066]
  Person re-identification (Re-ID) via gait features within 3D skeleton sequences is a newly-emerging topic with several advantages. This paper proposes a self-supervised gait encoding approach that can leverage unlabeled skeleton data to learn gait representations for person Re-ID.
  arXiv  Detail & Related papers  (2020-09-05T16:06:04Z)
• Skeleton Based Action Recognition using a Stacked Denoising Autoencoder with Constraints of Privileged Information [5.67220249825603]
  We propose a new method to study the skeletal representation in a view of skeleton reconstruction. Based on the concept of learning under privileged information, we integrate action categories and temporal coordinates into a stacked denoising autoencoder. In order to mitigate the variation resulting from temporary misalignment, a new method of temporal registration is proposed.
  arXiv  Detail & Related papers  (2020-03-12T09:56:22Z)

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.