4th Workshop on Maritime Computer Vision (MaCVi): Challenge Overview

2026-04-14

AI Summary

This work addresses the longstanding challenge in maritime computer vision of balancing prediction accuracy with embedded real-time performance by systematically treating both as co-optimization objectives for the first time. The study organized five benchmark challenges tailored to maritime visual tasks, leveraging a large-scale maritime vision dataset and a standardized evaluation protocol. Through comprehensive multidimensional assessment of real-time algorithms submitted by leading teams, the project not only uncovered prevailing trends and practical engineering insights but also publicly released quantitative results, qualitative comparisons, technical reports, the dataset, and leaderboards. These contributions have significantly advanced the practical deployment of maritime vision algorithms and fostered community-wide progress in the field.

Technology Category

• Computer Vision: Image and Video Retrieval
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• Maritime Computer Vision
• Benchmark Challenges
• Predictive Accuracy
• Real-time Feasibility
• CVPR

Innovation

• maritime computer vision
• real-time embedded systems
• benchmark challenges
• predictive accuracy
• edge deployment