OptiSAR-Net++: A Large-Scale Benchmark and Transformer-Free Framework for Cross-Domain Remote Sensing Visual Grounding

arXiv:2603.24876 · cs.CV · Submitted 2026-03-25 · Read on arXiv

cs.CV

Submitted: 2026-03-25

Updated: 2026-09-24

Terminology

• Grounding — 55× in this paper · explained in 3 episodes
• Visual Grounding — 35× in this paper · explained in 2 episodes
• Benchmark — 23× in this paper · explained in 2 episodes
• Transformer — 23× in this paper · explained in 2 episodes
• Adaptation — 12× in this paper · explained in 1 episode
• Contrastive Learning — 11× in this paper · explained in 5 episodes
• Alignment — 10× in this paper · explained in 1 episode
• Generalization — 10× in this paper · explained in 13 episodes
• Ablation — 8× in this paper · explained in 1 episode
• Accuracy — 6× in this paper · explained in 1 episode
• Decoupling — 6× in this paper · explained in 2 episodes
• Generalizability — 6× in this paper · explained in 6 episodes
• Verification — 5× in this paper · explained in 2 episodes
• Ablation Studies — 4× in this paper · explained in 1 episode
• Core — 4× in this paper · explained in 2 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• Computational overhead — 3× in this paper · explained in 2 episodes
• Domain Adaptation — 3× in this paper · explained in 5 episodes
• Mixture of Experts — 3× in this paper · explained in 2 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Aperture — 2× in this paper · explained in 1 episode
• Attention Mechanism — 2× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Transformer Architecture — 2× in this paper · explained in 2 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Edge Devices — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Instance Segmentation — 1× in this paper · explained in 3 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• Zero-Shot Transfer — 1× in this paper · explained in 3 episodes

Sources

• SM3Det: A Unified Model for Multi-Modal Remote Sensing Object Detection
• GPT-4 Technical Report
• YOLOE: Real-Time Seeing Anything
• M4-SAR: A Multi-Resolution, Multi-Polarization, Multi-Scene, Multi-Source Dataset and Benchmark for optical-SAR Object Detection
• A Comprehensive Survey of Mixture-of-Experts: Algorithms, Theory, and Applications
• Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

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