A2DINOv3: Rethinking Multi-Modal Object Detection via Socialized Collaboration

arXiv:2608.21099 · cs.CV, cs.AI · Submitted 2026-08-21 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-08-21

Updated: 2026-09-11

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Adaptation — 12× in this paper · explained in 1 episode
• Benchmark — 12× in this paper · explained in 2 episodes
• Foundation Model — 12× in this paper · explained in 3 episodes
• Foundation Models — 8× in this paper · explained in 7 episodes
• Transformer — 8× in this paper · explained in 2 episodes
• Socialized Collaboration Protocol — 7× in this paper
• Fine-Tuning — 6× in this paper · explained in 5 episodes
• Aggregation — 5× in this paper · explained in 1 episode
• Robustness — 5× in this paper · explained in 5 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Generalization — 4× in this paper · explained in 13 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Alignment — 3× in this paper · explained in 1 episode
• Chinese Association for Artificial Intelligence — 3× in this paper
• Ablation study — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Domain Shift — 2× in this paper · explained in 4 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Multimodal Fusion — 2× in this paper · explained in 3 episodes
• Quantization — 2× in this paper · explained in 12 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Transfer Learning — 2× in this paper · explained in 10 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Cooperation — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Local Illumination-aware Fusion — 1× in this paper

Sources

• Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models
• AdapterTune: Zero-Initialized Low-Rank Adapters for Frozen Vision Transformers
• Towards Efficient Visual Adaption via Structural Re-parameterization
• RF-DETR: Neural Architecture Search for Real-Time Detection Transformers
• LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention
• Rethinking Multi-Modal Object Detection from the Perspective of Mono-Modality Feature Learning

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