You Only Learn One Query: Learning Unified Human Query for Single-Stage Multi-Person Multi-Task Human-Centric Perception

TL;DR

This paper presents HQNet, a unified single-stage framework for multi-person multi-task human-centric perception, introducing a novel Human Query representation and a new benchmark dataset for comprehensive evaluation.

Contribution

The paper introduces a unified Human Query approach for multi-task perception and proposes the COCO-UniHuman benchmark dataset for evaluation.

Findings

Achieves state-of-the-art performance among multi-task models

Demonstrates competitive results with task-specific models

Shows Human Query's strong generalization to new tasks

Abstract

Human-centric perception (e.g. detection, segmentation, pose estimation, and attribute analysis) is a long-standing problem for computer vision. This paper introduces a unified and versatile framework (HQNet) for single-stage multi-person multi-task human-centric perception (HCP). Our approach centers on learning a unified human query representation, denoted as Human Query, which captures intricate instance-level features for individual persons and disentangles complex multi-person scenarios. Although different HCP tasks have been well-studied individually, single-stage multi-task learning of HCP tasks has not been fully exploited in the literature due to the absence of a comprehensive benchmark dataset. To address this gap, we propose COCO-UniHuman benchmark to enable model development and comprehensive evaluation. Experimental results demonstrate the proposed method's state-of-the-art performance among multi-task HCP models and its competitive performance compared to task-specific HCP models. Moreover, our experiments underscore Human Query's adaptability to new HCP tasks, thus demonstrating its robust generalization capability. Codes and data are available at https://github.com/lishuhuai527/COCO-UniHuman.