Position Aware Layer Queries for Test Time Training in Vision Language Models

arXiv:2609.34021 · cs.CV · Submitted 2026-09-27 · Read on arXiv

cs.CV

Submitted: 2026-09-27

Updated: 2026-09-27

Terminology

• Distillation — 25× in this paper · explained in 2 episodes
• Consistency — 22× in this paper · explained in 3 episodes
• Generalization — 22× in this paper · explained in 13 episodes
• Adaptation — 18× in this paper · explained in 1 episode
• Benchmark — 17× in this paper · explained in 2 episodes
• Regularization — 16× in this paper · explained in 3 episodes
• Accuracy — 13× in this paper · explained in 1 episode
• Test-Time Training — 12× in this paper
• Conference on Computer Vision and Pattern Recognition — 9× in this paper
• Location Consistency Regularization — 9× in this paper
• Position-Aware Distillation — 9× in this paper
• Distribution Shift — 8× in this paper · explained in 5 episodes
• Layer Query Network — 8× in this paper
• Test-Time Adaptation — 8× in this paper
• Robustness — 7× in this paper · explained in 5 episodes
• Embeddings — 5× in this paper · explained in 2 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Zero-Shot Generalization — 4× in this paper · explained in 5 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Core — 3× in this paper · explained in 2 episodes
• In International Conference on Machine Learning — 3× in this paper
• Knowledge Distillation — 3× in this paper · explained in 11 episodes
• Panoptic Quality — 3× in this paper
• Test-Time Prompt Tuning — 3× in this paper
• Vision-Language Models (VLMs) — 3× in this paper · explained in 6 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Information Leakage — 2× in this paper · explained in 2 episodes
• Self-consistency — 2× in this paper · explained in 2 episodes
• Accountability — 1× in this paper · explained in 2 episodes
• Corruption Robustness — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Few-Shot Learning — 1× in this paper · explained in 4 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• Teacher-Student Distillation — 1× in this paper · explained in 2 episodes
• Test-time Adaptation (TTA) — 1× in this paper · explained in 3 episodes

Sources

• Vision Language Models Are Few-Shot Audio Spectrogram Classifiers
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• GS-Bias: Global-Spatial Bias Learner for Single-Image Test-Time Adaptation of Vision-Language Models
• The Power of Scale for Parameter-Efficient Prompt Tuning
• Adaptive Cache Enhancement for Test-Time Adaptation of Vision-Language Models
• Test-Time Adaptation of Vision-Language Models for Open-Vocabulary Semantic Segmentation
• UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
• Adapter is All You Need for Tuning Visual Tasks
• CoCa: Contrastive Captioners are Image-Text Foundation Models

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