AttentionViG: Cross-Attention-Based Dynamic Neighbor Aggregation in Vision GNNs

arXiv:2509.25570 · cs.CV, cs.AI, eess.IV · Submitted 2025-09-29 · Read on arXiv

cs.CV, cs.AI, eess.IV

Submitted: 2025-09-29

Updated: 2026-09-27

Terminology

• Aggregation — 56× in this paper · explained in 1 episode
• Transformer — 24× in this paper · explained in 2 episodes
• Graph Construction — 22× in this paper · explained in 3 episodes
• Accuracy — 18× in this paper · explained in 1 episode
• Instance Segmentation — 12× in this paper · explained in 3 episodes
• Aggregation Function — 11× in this paper · explained in 1 episode
• GNNs — 9× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 8× in this paper
• Ablation — 4× in this paper · explained in 1 episode
• CVF International Conference on Computer Vision — 4× in this paper
• Cosine Similarity — 4× in this paper · explained in 3 episodes
• Convolutional Neural Networks (CNNs) — 3× in this paper · explained in 2 episodes
• Representation Learning — 3× in this paper · explained in 2 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Downstream Tasks — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Generalizability — 2× in this paper · explained in 6 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Hypergraph — 2× in this paper · explained in 2 episodes
• Transferability — 2× in this paper · explained in 4 episodes
• Adam — 1× in this paper · explained in 2 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Covariate Shift — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Graph Neural Networks (GNNs) — 1× in this paper · explained in 7 episodes
• Hybrid Models — 1× in this paper · explained in 2 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Sparse Attention — 1× in this paper · explained in 5 episodes
• Sparse Vision Graph Attention — 1× in this paper

Sources

• Gaussian Error Linear Units (GELUs)
• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
• ClusterViG: Efficient Globally Aware Vision GNNs via Image Partitioning

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