VesselBench-800K: A Large-scale Perception Benchmark for Multimodal Vessel Detection, Counting, and Density Estimation

arXiv:2609.37003 · cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.CV

Submitted: 2026-09-29

Updated: 2026-09-29

Code: https://github.com/danfenghong/IEEE

Terminology

• Benchmark — 28× in this paper · explained in 2 episodes
• Google Earth — 11× in this paper
• Robustness — 5× in this paper · explained in 5 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Feature Pyramid Networks — 3× in this paper
• Foundation Model — 3× in this paper · explained in 3 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 4 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Multimodal Fusion — 2× in this paper · explained in 3 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Annotation Ambiguity — 1× in this paper · explained in 1 episode
• Annotation Errors — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Data Fusion — 1× in this paper · explained in 3 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Instance Segmentation — 1× in this paper · explained in 3 episodes
• Temporal Correlations — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• UNICBench: UNIfied Counting Benchmark for MLLM
• Boosting Quantitive and Spatial Awareness for Zero-Shot Object Counting
• The QXS-SAROPT Dataset for Deep Learning in SAR-Optical Data Fusion
• AutoAssign: Differentiable Label Assignment for Dense Object Detection
• DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection
• Adam: A Method for Stochastic Optimization

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