MARINER: A 3E-Driven Benchmark for Fine-Grained Perception and Complex Reasoning in Open-Water Environments

arXiv cs.AI / 4/13/2026

💬 OpinionIdeas & Deep AnalysisModels & Research

共有:

Key Points

MARINER is a newly proposed 3E (Entity-Environment-Event) paradigm benchmark for fine-grained visual perception and complex reasoning in real-world open-water maritime scenes.
The dataset includes 16,629 multi-source maritime images, 63 vessel categories, adverse environmental conditions, and 5 dynamic maritime incident types, spanning fine-grained classification, object detection, and visual question answering.
Evaluations on mainstream multimodal large language models (MLLMs) and provided baselines show that current systems still struggle with fine-grained discrimination and causal reasoning in complex marine contexts.
The authors position MARINER as a dedicated, realistic benchmark to better measure cognitive-level maritime multimodal understanding and to drive research on more robust vision-language models for open-water applications.

Abstract

Fine-grained visual understanding and high-level reasoning in real-world open-water environments remain under-explored due to the lack of dedicated benchmarks. We introduce MARINER, a comprehensive benchmark built under the novel Entity-Environment-Event (3E) paradigm. MARINER contains 16,629 multi-source maritime images with 63 fine-grained vessel categories, diverse adverse environments, and 5 typical dynamic maritime incidents, covering fine-grained classification, object detection, and visual question answering tasks. We conduct extensive evaluations on mainstream Multimodal Large language models (MLLMs) and establish baselines, revealing that even advanced models struggle with fine-grained discrimination and causal reasoning in complex marine scenes. As a dedicated maritime benchmark, MARINER fills the gap of realistic and cognitive-level evaluation for maritime multimodal understanding, and promotes future research on robust vision-language models for open-water applications. Appendix and supplementary materials are available at https://lxixim.github.io/MARINER.