On the Robustness of Human-Object Interaction Detection against Distribution Shift

• URL: http://arxiv.org/abs/2506.18021v1
• Date: Sun, 22 Jun 2025 13:01:34 GMT
• Title: On the Robustness of Human-Object Interaction Detection against Distribution Shift
• Authors: Chi Xie, Shuang Liang, Jie Li, Feng Zhu, Rui Zhao, Yichen Wei, Shengjie Zhao,
• Abstract summary: Human-Object Interaction (HOI) detection has seen substantial advances in recent years.<n>Existing works focus on the standard setting with ideal images and natural distribution, far from practical scenarios with inevitable distribution shifts.<n>In this work, we investigate this issue by benchmarking, analyzing, and enhancing the robustness of HOI detection models under various distribution shifts.
• Score: 27.40641711088878
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Human-Object Interaction (HOI) detection has seen substantial advances in recent years. However, existing works focus on the standard setting with ideal images and natural distribution, far from practical scenarios with inevitable distribution shifts. This hampers the practical applicability of HOI detection. In this work, we investigate this issue by benchmarking, analyzing, and enhancing the robustness of HOI detection models under various distribution shifts. We start by proposing a novel automated approach to create the first robustness evaluation benchmark for HOI detection. Subsequently, we evaluate more than 40 existing HOI detection models on this benchmark, showing their insufficiency, analyzing the features of different frameworks, and discussing how the robustness in HOI is different from other tasks. With the insights from such analyses, we propose to improve the robustness of HOI detection methods through: (1) a cross-domain data augmentation integrated with mixup, and (2) a feature fusion strategy with frozen vision foundation models. Both are simple, plug-and-play, and applicable to various methods. Our experimental results demonstrate that the proposed approach significantly increases the robustness of various methods, with benefits on standard benchmarks, too. The dataset and code will be released.

Related papers

• RoHOI: Robustness Benchmark for Human-Object Interaction Detection [38.09248570129455]
  Human-Object Interaction (HOI) detection is crucial for robot-human assistance, enabling context-aware support.<n>We introduce the first benchmark for HOI detection, evaluating model resilience under diverse challenges.<n>Our benchmark, RoHOI, includes 20 corruption types based on HICO-DET and V-COCO datasets and a new robustness-focused metric.
  arXiv  Detail & Related papers  (2025-07-12T01:58:04Z)
• Ensemble-Based Deepfake Detection using State-of-the-Art Models with Robust Cross-Dataset Generalisation [0.0]
  Machine learning-based Deepfake detection models have achieved impressive results on benchmark datasets.<n>But their performance often deteriorates significantly when evaluated on out-of-distribution data.<n>In this work, we investigate an ensemble-based approach for improving the generalization of deepfake detection systems.
  arXiv  Detail & Related papers  (2025-07-08T13:54:48Z)
• Out-of-Distribution Detection on Graphs: A Survey [58.47395497985277]
  Graph out-of-distribution (GOOD) detection focuses on identifying graph data that deviates from the distribution seen during training.<n>We categorize existing methods into four types: enhancement-based, reconstruction-based, information propagation-based, and classification-based approaches.<n>We discuss practical applications and theoretical foundations, highlighting the unique challenges posed by graph data.
  arXiv  Detail & Related papers  (2025-02-12T04:07:12Z)
• Dissecting Out-of-Distribution Detection and Open-Set Recognition: A Critical Analysis of Methods and Benchmarks [17.520137576423593]
  We aim to provide a consolidated view of the two largest sub-fields within the community: out-of-distribution (OOD) detection and open-set recognition (OSR) We perform rigorous cross-evaluation between state-of-the-art methods in the OOD detection and OSR settings and identify a strong correlation between the performances of methods for them. We propose a new, large-scale benchmark setting which we suggest better disentangles the problem tackled by OOD detection and OSR.
  arXiv  Detail & Related papers  (2024-08-29T17:55:07Z)
• Combine and Conquer: A Meta-Analysis on Data Shift and Out-of-Distribution Detection [30.377446496559635]
  This paper introduces a universal approach to seamlessly combine out-of-distribution (OOD) detection scores. Our framework is easily for future developments in detection scores and stands as the first to combine decision boundaries in this context.
  arXiv  Detail & Related papers  (2024-06-23T08:16:44Z)
• Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmark [101.23684938489413]
  Anomaly detection (AD) is often focused on detecting anomalies for industrial quality inspection and medical lesion examination. This work first constructs a large-scale and general-purpose COCO-AD dataset by extending COCO to the AD field. Inspired by the metrics in the segmentation field, we propose several more practical threshold-dependent AD-specific metrics.
  arXiv  Detail & Related papers  (2024-04-16T17:38:26Z)
• Diffusion-Based Particle-DETR for BEV Perception [94.88305708174796]
  Bird-Eye-View (BEV) is one of the most widely-used scene representations for visual perception in Autonomous Vehicles (AVs) Recent diffusion-based methods offer a promising approach to uncertainty modeling for visual perception but fail to effectively detect small objects in the large coverage of the BEV. Here, we address this problem by combining the diffusion paradigm with current state-of-the-art 3D object detectors in BEV.
  arXiv  Detail & Related papers  (2023-12-18T09:52:14Z)
• Open World Object Detection in the Era of Foundation Models [53.683963161370585]
  We introduce a new benchmark that includes five real-world application-driven datasets. We introduce a novel method, Foundation Object detection Model for the Open world, or FOMO, which identifies unknown objects based on their shared attributes with the base known objects.
  arXiv  Detail & Related papers  (2023-12-10T03:56:06Z)
• Revisiting the Evaluation of Image Synthesis with GANs [55.72247435112475]
  This study presents an empirical investigation into the evaluation of synthesis performance, with generative adversarial networks (GANs) as a representative of generative models. In particular, we make in-depth analyses of various factors, including how to represent a data point in the representation space, how to calculate a fair distance using selected samples, and how many instances to use from each set.
  arXiv  Detail & Related papers  (2023-04-04T17:54:32Z)
• Boosting Out-of-Distribution Detection with Multiple Pre-trained Models [41.66566916581451]
  Post hoc detection utilizing pre-trained models has shown promising performance and can be scaled to large-scale problems. We propose a detection enhancement method by ensembling multiple detection decisions derived from a zoo of pre-trained models. Our method substantially improves the relative performance by 65.40% and 26.96% on the CIFAR10 and ImageNet benchmarks.
  arXiv  Detail & Related papers  (2022-12-24T12:11:38Z)
• CD-FSOD: A Benchmark for Cross-domain Few-shot Object Detection [0.0]
  We evaluate state-of-art FSOD approaches, including meta-learning FSOD approaches and fine-tuning FSOD approaches. Our approach is remarkably superior to existing approaches by significant margins.
  arXiv  Detail & Related papers  (2022-10-11T10:10:07Z)
• Benchmarking Deep Models for Salient Object Detection [67.07247772280212]
  We construct a general SALient Object Detection (SALOD) benchmark to conduct a comprehensive comparison among several representative SOD methods. In the above experiments, we find that existing loss functions usually specialized in some metrics but reported inferior results on the others. We propose a novel Edge-Aware (EA) loss that promotes deep networks to learn more discriminative features by integrating both pixel- and image-level supervision signals.
  arXiv  Detail & Related papers  (2022-02-07T03:43:16Z)

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.