4th Workshop on Maritime Computer Vision (MaCVi): Challenge Overview
Not on the map. Why?
Abstract (arXiv)
The 4th Workshop on Maritime Computer Vision (MaCVi) is organized as part of CVPR 2026. This edition features five benchmark challenges with emphasis on both predictive accuracy and embedded real-time feasibility. This report summarizes the MaCVi 2026 challenge setup, evaluation protocols, datasets, and benchmark tracks, and presents quantitative results, qualitative comparisons, and cross-challenge analyses of emerging method trends. We also include technical reports from top-performing teams to highlight practical design choices and lessons learned across the benchmark suite. Datasets, leaderboards, and challenge resources are available at https://macvi.org/workshop/cvpr26.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 35 of its 57 references that are in the map. First posted on arXiv 14 Apr 2026.
Builds on
35 of 57 references.
References · 35
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
- 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,764
- 2022A ConvNet for the 2020scited by 3,545
- 2024SAM 2: Segment Anything in Images and Videoscited by 2,784
- 2021Masked-attention Mask Transformer for Universal Image Segmentationcited by 2,338
- 2022DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detectioncited by 1,306
- 2019LVIS: A Dataset for Large Vocabulary Instance Segmentationcited by 1,215
- 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,064
- 2018Panoptic Segmentationcited by 796
- 2022Exploring Plain Vision Transformer Backbones for Object Detectioncited by 723
- 2021ImageNet-21K Pretraining for the Massescited by 607
- 2020Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentationcited by 595
23 more
- 2022DETRs with Collaborative Hybrid Assignments Trainingcited by 273
- 2023From Sparse to Soft Mixtures of Expertscited by 245
- 2022RTMDet: An Empirical Study of Designing Real-Time Object Detectorscited by 192
- 2015A Novel Performance Evaluation Methodology for Single-Target Trackerscited by 113
- 2022PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllerscited by 84
- 2023Dense Distinct Query for End-to-End Object Detectioncited by 76
- 2025RF-DETR: Neural Architecture Search for Real-Time Detection Transformerscited by 70
- 2025Your ViT is Secretly an Image Segmentation Modelcited by 61
- 2021SeaDronesSee: A Maritime Benchmark for Detecting Humans in Open Watercited by 59
- 2023WaterScenes: A Multi-Task 4D Radar-Camera Fusion Dataset and Benchmarks for Autonomous Driving on Water Surfacescited by 34
- 2025Real-Time Object Detection Meets DINOv3cited by 26
- 2021Leveraging Synthetic Data in Object Detection on Unmanned Aerial Vehiclescited by 22
- 2023LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and Benchmarkcited by 19
- 2024PhysAug: A Physical-guided and Frequency-based Data Augmentation for Single-Domain Generalized Object Detectioncited by 11
- 2023Mixed Pseudo Labels for Semi-Supervised Object Detectioncited by 10
- 2018Are object detection assessment criteria ready for maritime computer vision?cited by 9
- 2025MemorySAM: Memorize Modalities and Semantics with Segment Anything Model 2 for Multi-modal Semantic Segmentationcited by 9
- 2021Learning Maritime Obstacle Detection from Weak Annotations by Scaffoldingcited by 8
- 20253rd Workshop on Maritime Computer Vision (MaCVi) 2025: Challenge Resultscited by 4
- 2024PanSR: An Object-Centric Mask Transformer for Panoptic Segmentationcited by 3
- 2025Approximate Supervised Object Distance Estimation on Unmanned Surface Vehiclescited by 3
- 2025Real-Time Fusion of Visual and Chart Data for Enhanced Maritime Visioncited by 2
- 2025MULTIAQUA: A multimodal maritime dataset and robust training strategies for multimodal semantic segmentationcited by 1
Built on by
Not cited yet.