Kernel-Based Steering of CLIP with Vision-Language Model Preferences

arXiv:2609.32203 · cs.CV, cs.LG · Submitted 2026-09-26 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-09-26

Updated: 2026-09-26

Code: https://github.com/nightrome/cocostuff

Project page: https://snap-research.github.io/omni-attribute

Terminology

• Fidelity — 93× in this paper · explained in 2 episodes
• Adaptation — 81× in this paper · explained in 1 episode
• Benchmark — 45× in this paper · explained in 2 episodes
• Accuracy — 42× in this paper · explained in 1 episode
• Alignment — 35× in this paper · explained in 1 episode
• Embeddings — 28× in this paper · explained in 2 episodes
• Cosine Similarity — 15× in this paper · explained in 3 episodes
• Ablation — 12× in this paper · explained in 1 episode
• Vision-language models — 8× in this paper · explained in 2 episodes
• Consistency — 6× in this paper · explained in 3 episodes
• Distillation — 5× in this paper · explained in 2 episodes
• Steering — 4× in this paper · explained in 2 episodes
• Calibration — 3× in this paper · explained in 7 episodes
• Knowledge Distillation — 3× in this paper · explained in 11 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Contrastive Learning — 2× in this paper · explained in 5 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Instruction Tuning — 2× in this paper · explained in 2 episodes
• LoRA Adapters — 2× in this paper · explained in 3 episodes
• Personalization — 2× in this paper · explained in 2 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Vision-Language Models (VLMs) — 2× in this paper · explained in 6 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Anchor Loss — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Domain Adaptation — 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
• Frozen model — 1× in this paper · explained in 3 episodes
• LoRA Adaptation — 1× in this paper · explained in 2 episodes

Sources

• Qwen2.5-VL Technical Report
• Gemma 4 Technical Report
• Challenges in Representation Learning: A report on three machine learning contests
• FocalLens: Instruction Tuning Enables Zero-Shot Conditional Image Representations

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