CRISP: Compositional Relations as Invariant Structural Priors for Domain Generalization

arXiv:2605.06043 · cs.CV · Submitted 2026-05-07 · Read on arXiv

cs.CV

Submitted: 2026-05-07

Updated: 2026-09-23

Terminology

• Generalization — 35× in this paper · explained in 13 episodes
• Domain Generalization — 23× in this paper
• Accuracy — 15× in this paper · explained in 1 episode
• Adaptation — 13× in this paper · explained in 1 episode
• Benchmark — 12× in this paper · explained in 2 episodes
• Domain Adaptation — 10× in this paper · explained in 5 episodes
• Alignment — 9× in this paper · explained in 1 episode
• Regularization — 8× in this paper · explained in 3 episodes
• Pruning — 7× in this paper · explained in 4 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 5× in this paper
• In European Conference on Computer Vision — 4× in this paper
• Ablation Studies — 3× in this paper · explained in 1 episode
• Transformer — 3× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Compositional Generalization — 2× in this paper · explained in 4 episodes
• Computational overhead — 2× in this paper · explained in 2 episodes
• Data Augmentation — 2× in this paper · explained in 7 episodes
• Distribution Shift — 2× in this paper · explained in 5 episodes
• Domain Shift — 2× in this paper · explained in 4 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• In International Conference on Learning Representations — 2× in this paper
• Representation Learning — 2× in this paper · explained in 2 episodes
• Sparsity — 2× in this paper · explained in 4 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Adversarial Training — 1× in this paper · explained in 1 episode
• Distribution Matching — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Meta-learning — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• Invariant Risk Minimization
• Adam: A Method for Stochastic Optimization
• Conditional Adversarial Domain Adaptation
• The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
• Numerical Coordinate Regression with Convolutional Neural Networks
• ERM++: An Improved Baseline for Domain Generalization
• mixup: Beyond Empirical Risk Minimization
• Domain Generalization with MixStyle

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