Not All Patches Are Equally Forgettable: Spatially Localized Domain Unlearning in Vision-Language Models

arXiv:2609.17790 · cs.CV · Submitted 2026-09-15 · Read on arXiv

cs.CV

Submitted: 2026-09-15

Updated: 2026-09-15

Terminology

• Transformer — 26× in this paper · explained in 2 episodes
• Vision-language models — 14× in this paper · explained in 2 episodes
• Accuracy — 12× in this paper · explained in 1 episode
• Adaptation — 11× in this paper · explained in 1 episode
• Generalization — 9× in this paper · explained in 13 episodes
• Aggregation — 8× in this paper · explained in 1 episode
• Localized Domain Effect — 8× in this paper
• Domain Adaptation — 7× in this paper · explained in 5 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Alignment — 6× in this paper · explained in 1 episode
• Machine Unlearning — 6× in this paper · explained in 2 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Distribution Shift — 4× in this paper · explained in 6 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• Ablation study — 3× in this paper · explained in 1 episode
• Robustness — 3× in this paper · explained in 5 episodes
• Vision-Language Models (VLMs) — 3× in this paper · explained in 6 episodes
• Black-Box Forgetting — 2× in this paper
• Distillation — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• Transferability — 2× in this paper · explained in 4 episodes
• Activation Steering — 1× in this paper · explained in 4 episodes
• Approximate Unlearning — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Linear probe — 1× in this paper · explained in 2 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Steering — 1× in this paper · explained in 2 episodes

Sources

• Understanding intermediate layers using linear classifier probes
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Machine Unlearning of Features and Labels
• CoCa: Contrastive Captioners are Image-Text Foundation Models

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