Spatio-Temporal Proximity-Aware Dual-Path Model for Panoramic Activity Recognition
- URL: http://arxiv.org/abs/2403.14113v1
- Date: Thu, 21 Mar 2024 03:56:24 GMT
- Title: Spatio-Temporal Proximity-Aware Dual-Path Model for Panoramic Activity Recognition
- Authors: Sumin Lee, Yooseung Wang, Sangmin Woo, Changick Kim,
- Abstract summary: Panoramic Activity Recognition (PAR) seeks to identify human activities across different scales.
Social Proximity-aware Dual-Path Network (S PDP-Net) based on two key design principles.
S PDP-Net achieves new state-of-the-art performance with 46.5% of overall F1 score on JRDB-PAR dataset.
- Score: 19.813895376349613
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Panoramic Activity Recognition (PAR) seeks to identify diverse human activities across different scales, from individual actions to social group and global activities in crowded panoramic scenes. PAR presents two major challenges: 1) recognizing the nuanced interactions among numerous individuals and 2) understanding multi-granular human activities. To address these, we propose Social Proximity-aware Dual-Path Network (SPDP-Net) based on two key design principles. First, while previous works often focus on spatial distance among individuals within an image, we argue to consider the spatio-temporal proximity. It is crucial for individual relation encoding to correctly understand social dynamics. Secondly, deviating from existing hierarchical approaches (individual-to-social-to-global activity), we introduce a dual-path architecture for multi-granular activity recognition. This architecture comprises individual-to-global and individual-to-social paths, mutually reinforcing each other's task with global-local context through multiple layers. Through extensive experiments, we validate the effectiveness of the spatio-temporal proximity among individuals and the dual-path architecture in PAR. Furthermore, SPDP-Net achieves new state-of-the-art performance with 46.5\% of overall F1 score on JRDB-PAR dataset.
Related papers
- MPT-PAR:Mix-Parameters Transformer for Panoramic Activity Recognition [2.1794550051087995]
We propose a model called MPT-PAR that considers both the unique characteristics of each task and the synergies between different tasks simultaneously.
Our method achieved granularity and an overall F1 score of 47.5% on the JRDB-PAR dataset.
arXiv Detail & Related papers (2024-08-01T09:42:44Z) - Detecting Any Human-Object Interaction Relationship: Universal HOI
Detector with Spatial Prompt Learning on Foundation Models [55.20626448358655]
This study explores the universal interaction recognition in an open-world setting through the use of Vision-Language (VL) foundation models and large language models (LLMs)
Our design includes an HO Prompt-guided Decoder (HOPD), facilitates the association of high-level relation representations in the foundation model with various HO pairs within the image.
For open-category interaction recognition, our method supports either of two input types: interaction phrase or interpretive sentence.
arXiv Detail & Related papers (2023-11-07T08:27:32Z) - Spatial-Temporal Knowledge-Embedded Transformer for Video Scene Graph
Generation [64.85974098314344]
Video scene graph generation (VidSGG) aims to identify objects in visual scenes and infer their relationships for a given video.
Inherently, object pairs and their relationships enjoy spatial co-occurrence correlations within each image and temporal consistency/transition correlations across different images.
We propose a spatial-temporal knowledge-embedded transformer (STKET) that incorporates the prior spatial-temporal knowledge into the multi-head cross-attention mechanism.
arXiv Detail & Related papers (2023-09-23T02:40:28Z) - Two-stream Multi-level Dynamic Point Transformer for Two-person Interaction Recognition [45.0131792009999]
We propose a point cloud-based network named Two-stream Multi-level Dynamic Point Transformer for two-person interaction recognition.
Our model addresses the challenge of recognizing two-person interactions by incorporating local-region spatial information, appearance information, and motion information.
Our network outperforms state-of-the-art approaches in most standard evaluation settings.
arXiv Detail & Related papers (2023-07-22T03:51:32Z) - A Hierarchical Interactive Network for Joint Span-based Aspect-Sentiment
Analysis [34.1489054082536]
We propose a hierarchical interactive network (HI-ASA) to model two-way interactions between two tasks appropriately.
We use cross-stitch mechanism to combine the different task-specific features selectively as the input to ensure proper two-way interactions.
Experiments on three real-world datasets demonstrate HI-ASA's superiority over baselines.
arXiv Detail & Related papers (2022-08-24T03:03:49Z) - Dual-AI: Dual-path Actor Interaction Learning for Group Activity
Recognition [103.62363658053557]
We propose a Dual-path Actor Interaction (DualAI) framework, which flexibly arranges spatial and temporal transformers.
We also introduce a novel Multi-scale Actor Contrastive Loss (MAC-Loss) between two interactive paths of Dual-AI.
Our Dual-AI can boost group activity recognition by fusing distinct discriminative features of different actors.
arXiv Detail & Related papers (2022-04-05T12:17:40Z) - DRG: Dual Relation Graph for Human-Object Interaction Detection [65.50707710054141]
We tackle the challenging problem of human-object interaction (HOI) detection.
Existing methods either recognize the interaction of each human-object pair in isolation or perform joint inference based on complex appearance-based features.
In this paper, we leverage an abstract spatial-semantic representation to describe each human-object pair and aggregate the contextual information of the scene via a dual relation graph.
arXiv Detail & Related papers (2020-08-26T17:59:40Z) - DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act
Recognition and Sentiment Classification [77.59549450705384]
In dialog system, dialog act recognition and sentiment classification are two correlative tasks.
Most of the existing systems either treat them as separate tasks or just jointly model the two tasks.
We propose a Deep Co-Interactive Relation Network (DCR-Net) to explicitly consider the cross-impact and model the interaction between the two tasks.
arXiv Detail & Related papers (2020-08-16T14:13:32Z) - Cascaded Human-Object Interaction Recognition [175.60439054047043]
We introduce a cascade architecture for a multi-stage, coarse-to-fine HOI understanding.
At each stage, an instance localization network progressively refines HOI proposals and feeds them into an interaction recognition network.
With our carefully-designed human-centric relation features, these two modules work collaboratively towards effective interaction understanding.
arXiv Detail & Related papers (2020-03-09T17:05:04Z)
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.