Why Domain Matters: Domain-Aware Benchmarking of Underwater Object Detection and Annotation Quality
- URL: http://arxiv.org/abs/2607.10575v1
- Date: Sun, 12 Jul 2026 05:11:59 GMT
- Title: Why Domain Matters: Domain-Aware Benchmarking of Underwater Object Detection and Annotation Quality
- Abstract summary: We introduce a framework that characterizes underwater images by appearance, scene composition, and acquisition geometry to assign domain labels.<n>We perform the first systematic study of how domain factors influence both human annotation quality in underwater object detection datasets and deep learning-based detector performance.<n>We highlight how this can be used to guide data collection and annotation, design more informative benchmarks, and assess detector robustness across diverse underwater environments.
- Score: 9.468095570700642
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Underwater object detection is strongly affected by domain shift, where performance can vary significantly across different locations, habitats, and deployment conditions. However, detector performance is typically evaluated using aggregate metrics that hide failures in specific environments, while existing domain generalization benchmarks often rely on synthetic variations that do not reflect real-world conditions. We introduce a framework that characterizes underwater images by appearance, scene composition, and acquisition geometry to assign domain labels. Using this framework, we perform the first systematic study of how domain factors influence both human annotation quality in underwater object detection datasets and deep learning-based detector performance, revealing substantial domain-dependent discrepancies. By incorporating physically meaningful domain labels, domain shift becomes something we can characterize, measure, benchmark, and act on. We highlight how this can be used to guide data collection and annotation, design more informative benchmarks, and assess detector robustness across diverse underwater environments.
Related papers
- Why Domain Matters: A Preliminary Study of Domain Effects in Underwater Object Detection [9.468095570700642]
We propose a labeling framework that defines underwater domains using measurable image, scene, and acquisition characteristics.<n>Unlike prior benchmarks, it captures physically meaningful factors, enabling semantically consistent image grouping and supporting domain-specific evaluation of detection performance including failure analysis.
arXiv Detail & Related papers (2026-04-28T23:28:14Z) - Exploiting Aggregation and Segregation of Representations for Domain Adaptive Human Pose Estimation [50.31351006532924]
Human pose estimation (HPE) has received increasing attention recently due to its wide application in motion analysis, virtual reality, healthcare, etc.<n>It suffers from the lack of labeled diverse real-world datasets due to the time- and labor-intensive annotation.<n>We introduce a novel framework that capitalizes on both representation aggregation and segregation for domain adaptive human pose estimation.
arXiv Detail & Related papers (2024-12-29T17:59:45Z) - Domain Similarity-Perceived Label Assignment for Domain Generalized
Underwater Object Detection [18.39732229138302]
This paper introduces the concept of Domain Similarity-Perceived Label Assignment (DSP)
Through domain-specific data augmentation techniques, we achieved state-of-the-art results on the underwater cross-domain object detection benchmark S-UODAC 2020.
arXiv Detail & Related papers (2023-12-20T05:45:56Z) - ADOD: Adaptive Domain-Aware Object Detection with Residual Attention for
Underwater Environments [1.2624532490634643]
This research presents ADOD, a novel approach to address domain generalization in underwater object detection.
Our method enhances the model's ability to generalize across diverse and unseen domains, ensuring robustness in various underwater environments.
arXiv Detail & Related papers (2023-12-11T19:20:56Z) - ATTA: Anomaly-aware Test-Time Adaptation for Out-of-Distribution
Detection in Segmentation [22.084967085509387]
We propose a dual-level OOD detection framework to handle domain shift and semantic shift jointly.
The first level distinguishes whether domain shift exists in the image by leveraging global low-level features.
The second level identifies pixels with semantic shift by utilizing dense high-level feature maps.
arXiv Detail & Related papers (2023-09-12T06:49:56Z) - Towards Generalization on Real Domain for Single Image Dehazing via
Meta-Learning [41.99615673136883]
Internal information learned from synthesized images is usually sub-optimal in real domains.
We present a domain generalization framework based on meta-learning to dig out representative internal properties of real hazy domains.
Our proposed method has superior generalization ability than the state-of-the-art competitors.
arXiv Detail & Related papers (2022-11-14T07:04:00Z) - Domain Adaptation for Underwater Image Enhancement [51.71570701102219]
We propose a novel Two-phase Underwater Domain Adaptation network (TUDA) to minimize the inter-domain and intra-domain gap.
In the first phase, a new dual-alignment network is designed, including a translation part for enhancing realism of input images, followed by an enhancement part.
In the second phase, we perform an easy-hard classification of real data according to the assessed quality of enhanced images, where a rank-based underwater quality assessment method is embedded.
arXiv Detail & Related papers (2021-08-22T06:38:19Z) - AFAN: Augmented Feature Alignment Network for Cross-Domain Object
Detection [90.18752912204778]
Unsupervised domain adaptation for object detection is a challenging problem with many real-world applications.
We propose a novel augmented feature alignment network (AFAN) which integrates intermediate domain image generation and domain-adversarial training.
Our approach significantly outperforms the state-of-the-art methods on standard benchmarks for both similar and dissimilar domain adaptations.
arXiv Detail & Related papers (2021-06-10T05:01:20Z) - Multi-Target Domain Adaptation via Unsupervised Domain Classification
for Weather Invariant Object Detection [1.773576418078547]
The performance of an object detector significantly degrades if the weather of the training images is different from that of test images.
We propose a novel unsupervised domain classification method which can be used to generalize single-target domain adaptation methods to multi-target domains.
We conduct the experiments on Cityscapes dataset and its synthetic variants, i.e. foggy, rainy, and night.
arXiv Detail & Related papers (2021-03-25T16:59:35Z) - Unsupervised Domain Adaptation for Spatio-Temporal Action Localization [69.12982544509427]
S-temporal action localization is an important problem in computer vision.
We propose an end-to-end unsupervised domain adaptation algorithm.
We show that significant performance gain can be achieved when spatial and temporal features are adapted separately or jointly.
arXiv Detail & Related papers (2020-10-19T04:25:10Z) - Phase Consistent Ecological Domain Adaptation [76.75730500201536]
We focus on the task of semantic segmentation, where annotated synthetic data are aplenty, but annotating real data is laborious.
The first criterion, inspired by visual psychophysics, is that the map between the two image domains be phase-preserving.
The second criterion aims to leverage ecological statistics, or regularities in the scene which are manifest in any image of it, regardless of the characteristics of the illuminant or the imaging sensor.
arXiv Detail & Related papers (2020-04-10T06:58:03Z) - Spatial Attention Pyramid Network for Unsupervised Domain Adaptation [66.75008386980869]
Unsupervised domain adaptation is critical in various computer vision tasks.
We design a new spatial attention pyramid network for unsupervised domain adaptation.
Our method performs favorably against the state-of-the-art methods by a large margin.
arXiv Detail & Related papers (2020-03-29T09:03:23Z)
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.