4th Workshop on Maritime Computer Vision (MaCVi): Challenge Overview
Authors: Benjamin Kiefer, Jan Lukas Augustin, Jon Muhovič, Mingi Jeong, Arnold Wiliem, Janez Pers, Matej Kristan, Alberto Quattrini Li, Matija Teršek, Josip Šarić, Arpita Vats, Dominik Hildebrand, Rafia Rahim, Mahmut Karaaslan, Arpit Vaishya, Steve Xie, Ersin Kaya, Akib Mashrur, Tze-Hsiang Tang, Chun-Ming Tsai, Jun-Wei Hsieh, Ming-Ching Chang, Wonwoo Jo, Doyeon Lee, Yusi Cao, Lingling Li, Vinayak Nageli, Arshad Jamal, Gorthi Rama Krishna Sai Subrahmanyam, Jemo Maeng, Seongju Lee, Kyoobin Lee, Xu Liu, LiCheng Jiao, Jannik Sheikh, Martin Weinmann, Ivan Martinović, Jose Mateus Raitz Persch, Rahul Harsha Cheppally, Mehmet E. Belviranli, Dimitris Gahtidis, Hyewon Chun, Sangmun Lee, Philipp Gorczak, Hansol Kim, Jeeyeon Jeon, Borja Carrillo Perez, Jiahui Wang, Sangmin Park, Andreas Michel, Jannick Kuester, Bettina Felten, Wolfgang Gross, Yuan Feng, Justin Davis
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.
Figures & tables
Figure 1: Qualitative detection examples from the Vision-to-Chart Data Association challenge. Each panel shows the overlay of predicted and ground-truth bounding boxes and predicted index of associated chart marker.
Table 1: Results for the Vision-to-Chart Data Association challenge on the held out test split. Ranking medals indicate final placement. The baseline (gray) is the organizer-provided DETR-style fusion transformer. Method names are derived from the submitted technical reports.
Table 2: Results for the Thermal Object Detection challenge. Ranking medals indicate final placement. The baseline (gray) is the organizer-provided Faster R-CNN with ResNet-50. Self-reported inference speed and hardware are included. Backbone and detection-level pretraining datasets are reported separately (IN = ImageNet-1K, IN-22K = ImageNet-22K, LVD = Large Vision Dataset).
Figure 2: Per-class and per-size AP breakdown for the Thermal Object Detection challenge. Left: AP for vessel vs. navigational object classes. Right: AP at COCO size thresholds (small / medium / large). Vessel detection is consistently easier than navigational object detection across all methods. Small object AP remains the main bottleneck.
Figure 3: Qualitative detection examples from the Thermal Object Detection challenge. Each row shows one test image with ground truth (green) and predictions from the top three methods and baseline. Columns left to right: ground truth, 1st place, 2nd place, 3rd place, baseline. Predictions are filtered at confidence ≥0.3 . V = vessel, N = navigational object.
Figure 4: Precision–Recall curves for the Thermal Object Detection challenge. Curves are shown for each method at IoU=0.50.
Table 3: Results for the USV-based Panoptic Segmentation Challenge measured in overall panoptic quallity (PQ) and separate for thing and stuff classes. Competing methods are compared to a baseline Mask2Former model [ 5 ] and the top-performing PanSR method [ 59 ] .
Figure 5: Panoptic quality stratified according to scene attributes.
Figure 6: Dynamic obstacle detection rate stratified according to object size.
Figure 7: Qualitative results of the top-performing methods in the LaRS panoptic segmentation challenge.
Table 4: Overview of the submissions for the USV-based Embedded Obstacle Segmentation challenge. For comparison, last year’s winning method is included inline in gray and is not assigned placements. The best results among current non-gray entries are denoted in bold.
Figure 8: Detection rate and FP as a function of obstacle size for embedded segmentation methods. Only the three winning and the best-performing method from last year’s challenge are shown.
Figure 9: Detection F1 under challenging visual conditions for embedded segmentation methods. Only the three winning and the best-performing method from last year’s challenge are shown.
Figure 10: Detection F1 across scene environments and reflection levels for embedded segmentation methods showing the three winning and the best-performing method from last year’s challenge.
Table 5: Results for the USV-based Multimodal Segmentation Challenge. We report M , validation/test mIoU, and dynamic-obstacle IoU.
Appendix figures & tables3 assets
Supplementary material from the paper’s appendix.
Appendix
Method
PQ
SQ
RQ
Mask2Former + DINOv3-L
54.8
75.8
63.5
Mask2Former + DINOv3-H+
55.8
75.5
65.4
Appendix
Table 6: Panoptic segmentation results on the LARS validation set.
Figure 11: Training pipeline. WS = WaterScenes, RB = ROSEBUD, MA = MULTIAQUA_night.
Figure 12: Overview of GatedMemorySAM. Each modality is encoded by SAM2 with Soft-MoE LoRA, scored by the CrossModalFusionHead, and fused via memory modulation and weighted mask fusion.
The IEEE Low-Power Computer Vision Challenge (LPCVC) aims to promote the development of efficient vision models for edge devices, balancing accuracy with constraints such as latency, memory capacity, and energy use. The 2025 challenge featured three tracks: (1) Image classification under various lighting conditions and styles, (2) Open-Vocabulary Segmentation with Text Prompt, and (3) Monocular Depth Estimation. This paper presents the design of LPCVC 2025, including its competition structure and evaluation framework, which integrates the Qualcomm AI Hub for consistent and reproducible benchmarking. The paper also introduces the top-performing solutions from each track and outlines key trends and observations. The paper concludes with suggestions for future computer vision competitions.
Zihao Ye, Yung-Hsiang Lu, Xiao Hu +14
Purdue University, West Lafayette, IN, USA · Qualcomm, USA · Lehigh University, Bethlehem, PA, USA +3
This report summarizes the objectives, datasets, and top-performing methodologies of the 2026 Pixel-level Video Understanding in the Wild (PVUW) Challenge, hosted at CVPR 2026, which evaluates state-of-the-art models under highly unconstrained conditions. To provide a comprehensive assessment, the 2026 edition features three specialized tracks: the MOSE track for tracking objects within densely cluttered and severely occluded scenarios; the MeViS-Text track for localizing targets via motion-focused linguistic expressions; and the newly inaugurated MeViS-Audio track, which pioneers acoustic-driven object segmentation. By introducing previously unreleased challenging data and analyzing the cutting-edge, multimodal solutions submitted by participants, this report highlights the community's latest technical advancements and charts promising future directions for robust video scene comprehension.
This paper presents the report of the URVIS 2026 challenge on adverse-to-extreme panoptic segmentation. As the first challenge of its kind, it attracted 17 registered participants and 47 submissions, with 4 teams reaching the final phase. The challenge is based on the MUSES dataset, a multi-sensor benchmark for panoptic segmentation in adverse-to-extreme weather, including RGB frame camera, LiDAR, radar, and event camera data. Weighted Panoptic Quality (wPQ) is designed and adopted as the official ranking metric for fair evaluation across weather conditions. In this report, we summarise the challenge setting and benchmark results, analyse the performance of the submitted methods, and discuss current progress and remaining challenges for robust multimodal panoptic segmentation. Link: https://urvis-workshop.github.io/challenge-Muses.html
Yiting Wang, Nolwenn Peyratout, Tim Brodermann +13