MARINER: A 3E-Driven Benchmark for Fine-Grained Perception and Complex Reasoning in Open-Water Environments
This addresses the problem of evaluating realistic and cognitive-level maritime multimodal understanding for researchers in computer vision and AI, though it is incremental as it introduces a new benchmark rather than a novel method.
The authors tackled the lack of dedicated benchmarks for fine-grained visual understanding and reasoning in open-water environments by introducing MARINER, a comprehensive benchmark with 16,629 multi-source maritime images covering 63 vessel categories and 5 dynamic incidents, which revealed that even advanced multimodal models struggle with fine-grained discrimination and causal reasoning in these complex scenes.
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.