DeCo: Efficient Decouple-to-Couple Learning for Multi-Task Visual Grounding

arXiv:2609.24409 · cs.CV · Submitted 2026-09-21 · Read on arXiv

cs.CV

Submitted: 2026-09-21

Updated: 2026-09-21

Code: https://github.com/xiaoqiang-lu/DeCo

Terminology

• Grounding — 70× in this paper · explained in 3 episodes
• Visual Grounding — 45× in this paper · explained in 2 episodes
• Benchmark — 34× in this paper · explained in 2 episodes
• Transformer — 28× in this paper · explained in 2 episodes
• Adaptation — 20× in this paper · explained in 1 episode
• Decoupling — 17× in this paper · explained in 2 episodes
• Generalization — 12× in this paper · explained in 13 episodes
• Ablation — 10× in this paper · explained in 1 episode
• Alignment — 9× in this paper · explained in 1 episode
• Fine-Tuning — 8× in this paper · explained in 5 episodes
• Embeddings — 7× in this paper · explained in 2 episodes
• Parameter-Efficient Fine-Tuning — 7× in this paper · explained in 2 episodes
• Hybrid Prior Coupling — 5× in this paper
• Robustness — 5× in this paper · explained in 5 episodes
• Task-aware Semantic Decoupling — 5× in this paper
• Consistency — 4× in this paper · explained in 3 episodes
• Core — 4× in this paper · explained in 2 episodes
• Ablation Studies — 3× in this paper · explained in 1 episode
• Adam — 3× in this paper · explained in 2 episodes
• Foundation Model — 3× in this paper · explained in 3 episodes
• Foundation Models — 3× in this paper · explained in 7 episodes
• Instruction Tuning — 3× in this paper · explained in 2 episodes
• Representation Learning — 3× in this paper · explained in 2 episodes
• Annotation Ambiguity — 2× in this paper · explained in 1 episode
• Computational overhead — 2× in this paper · explained in 2 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Low-Rank Adaptation (LoRA) — 2× in this paper · explained in 9 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adaptive Selection — 1× in this paper · explained in 1 episode
• Annotation Errors — 1× in this paper · explained in 1 episode
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Cooperation — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Localization-oriented Model — 1× in this paper
• Loss Function — 1× in this paper · explained in 2 episodes
• Mask Generation — 1× in this paper · explained in 2 episodes
• Multimodal Fusion — 1× in this paper · explained in 3 episodes
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Parameter-Efficient Fine-Tuning (PEFT) — 1× in this paper · explained in 4 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• Segmentation-oriented Model — 1× in this paper
• Spectral Geometry — 1× in this paper · explained in 2 episodes
• Test-time Scaling — 1× in this paper · explained in 2 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling
• Multimodal Referring Segmentation: A Survey
• DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding

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