Hugging Face Trending Papers
Test-Time Logit Prompting for Source-Free Missing Modality Adaptation
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arXiv Computer Vision
Sep 3
Test-Time Logit Prompting for Source-Free Missing Modality Adaptation
The paper introduces Test-Time Logit Prompting (TLP), a lightweight framework that adapts vision-language models to missing-modality inputs without accessing source training data. TLP optimizes logit prompts using uncertainty-aware adjustments and modality-complete consistency regularization, thereby maintaining prediction confidence and semantic consistency. Experiments on various benchmarks show that TLP improves recognition performance by up to 8% while requiring only a few hundred tunable parameters and minimal test-time optimization steps.
arXiv Computer Vision
Sep 1
GATE: Reliability-Gated Gaussian Evidence Fusion for Training-Free Test-Time Adaptation of Vision-Language Models
arXiv:2608.29395v1 Announce Type: new Abstract: Vision-language models such as CLIP and SigLIP provide strong zero-shot recognition, but their predictions can degrade when deployed on target data tha...
arXiv AI
Jun 8
SS-TPT: Stability and Suitability-Guided Test-Time Prompt Tuning for Adversarially Robust Vision-Language Models
arXiv:2606. 06943v1 Announce Type: cross Abstract: Vision-language models (VLMs) such as CLIP achieve strong zero-shot recognition but remain highly fragile under adversarial perturbations.
Hugging Face Trending Papers
Jul 22
Test-Time Training for Modality Order Consistency in Vision-Language Models
We find that vision-language models are sensitive to a specific semantically irrelevant change: the order in which the image and question are presented. Across three models and three benchmarks, image first prompting consistently outperforms question-first prompting, revealing a repeatable modality order failure.
arXiv AI
Aug 20
From Inference to Adaptation: A Unified Optimal Transport View of Vision Language Model
arXiv:2608. 18339v1 Announce Type: cross Abstract: Vision-language models (VLMs) have demonstrated remarkable zero-shot capabilities yet remain sensitive to real-world distribution shifts during inference.
arXiv AI
Jul 13
TCLA: Training-Free Class-wise Logit Adaptation for Medical Vision-Language Models
arXiv:2607. 09562v1 Announce Type: cross Abstract: Medical Vision-Language Models (VLMs) exhibit strong zero-shot performance, yet their effectiveness still declines on out-of-distribution (OOD) data due to domain shifts and class bias inherited from large-scale pretraining.