arXiv AI
Sep 16

Sparse MLLM Anchors, Dense Adaptation: Breaking the Self-Referential Loop in Wild Test-Time Adaptation

The paper introduces MASA, a method for Wild Test-Time Adaptation that uses a frozen multimodal large language model to provide structured semantic anchors, thereby avoiding the self-referential loop common in existing WTTA techniques. MASA selects a small, diverse set of reliability-ranked anchors, encodes their descriptions, propagates them to nearby test samples, and stores this visual‑semantic information in an online prototype memory. The stored descriptors enable lightweight adaptation of normalization parameters, and MASA is evaluated on the WTTA ImageNet‑C benchmark with ResNet and ViT backbones under limited‑batch, mixed‑domain, and imbalanced‑label‑shift scenarios.

By Zhenbin Wang, Lei Zhang, Lituan Wang, Yan Wang, Zhao Zhang, Wei Huang
arXiv AI
4d ago

Batch-Conditioned Semantic Anchors for Robust Transductive Adaptation of Vision--Language Models

Batch-Conditioned Semantic Anchors for Robust Transductive Adaptation of Vision–Language Models proposes JPTA, a prior-aware framework that refines vision-language model predictions at test time by estimating batch-conditioned semantic references. Unlike aggressive methods that risk drift on sparse or shifted batches, or conservative methods that may under-adapt, JPTA calibrates the influence of CLIP text prototypes based on batch reliability, yielding consistent performance gains across benchmarks, especially in sparse and prior-shifted scenarios.

By Mohammed Rahman Sherif Khan Mohammad, Ardhendu Behera, Sandip Pradhan, Swagat Kumar, Amr Ahmed
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.

By Taixi Chen, Nancy Guo