GATE: Reliability-Gated Gaussian Evidence Fusion for Training-Free Test-Time Adaptation of Vision-Language Models

• URL: http://arxiv.org/abs/2608.29395v1
• Date: Sat, 29 Aug 2026 18:33:23 GMT
• Title: GATE: Reliability-Gated Gaussian Evidence Fusion for Training-Free Test-Time Adaptation of Vision-Language Models
• Abstract summary: GATE is a training-free two-pass transductive test-time adaptation framework.<n>It uses the unlabeled target set while keeping the image encoder, text encoder, and prompt parameters fully frozen.<n>It improves zero-shot performance by an average of 5.41 points and outperforms the strongest non-GATE baseline by 1.94 points.
• Score: 5.025577782616721
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-language models such as CLIP and SigLIP provide strong zero-shot recognition, but their predictions can degrade when deployed on target data that differ from the pretraining distribution. Test-time adaptation offers a practical way to improve robustness without source data or target labels, yet existing methods often rely on either prompt-side adaptation or image-side target evidence alone. In this work, we introduce GATE, a training-free two-pass transductive test-time adaptation framework that uses the unlabeled target set while keeping the image encoder, text encoder, and prompt parameters fully frozen. Instead of representing each class with a single prototype, GATE builds two complementary Gaussian sources of evidence in the shared vision-language feature space: a text Gaussian estimated from multiple language descriptions and an image Gaussian estimated from reliable unlabeled target samples. A class-wise reliability gate controls the influence of image-derived pseudo-evidence, and a score-level generalized Product-of-Experts fusion produces a normalized residual correction to the original zero-shot logits. Across fine-grained recognition datasets, ImageNet-family distribution shifts, multiple CLIP backbones, and SigLIP-B/16, GATE achieves the best average accuracy in every benchmark/backbone group. It improves zero-shot performance by an average of 5.41 points and outperforms the strongest non-GATE baseline by 1.94 points, demonstrating the benefit of reliability-gated distributional evidence for frozen VLM adaptation.

Related papers

• RINSE: Robust Target-Time Normality Estimation for Zero-Shot Graph Anomaly Detection [9.187714394723931]
  RINSE (Robust Iterative Normality Self-Estimation) is a gradient-free target-time framework.<n>It estimates target normality, representation calibration, and evidence reliability from the target graph.<n>These results support robust target-time estimation as a practical approach to generalist graph anomaly detection.
  arXiv  Detail & Related papers  (2026-09-02T12:03:58Z)
• Self-Improving In-Context Learning [3.9202238580555417]
  We propose to improve in-context learning by optimizing the embeddings of a few prompt test at time.<n>We formalize this signal as a bounded, self-supervised calibration procedure.<n>Across a comprehensive suite of ICL tasks, the proposed calibration improves or matches upon the base model and consistently outperforms classification-specific baselines on most tasks.
  arXiv  Detail & Related papers  (2026-05-22T03:01:34Z)
• Dual-Teacher Distillation with Subnetwork Rectification for Black-Box Domain Adaptation [69.7036779439312]
  Black box domain adaptation represents a highly practical yet extremely challenging setting.<n>We propose a dual teacher distillation with subnetwork rectification (DDSR) model that exploits specific knowledge embedded in black box source models.<n>Our approach demonstrates consistent improvements over state of the art methods, including those using source data or models.
  arXiv  Detail & Related papers  (2026-03-24T07:54:19Z)
• Intra-Class Probabilistic Embeddings for Uncertainty Estimation in Vision-Language Models [7.5752750293638735]
  We introduce a training-free, post-hoc uncertainty estimation method for contrastive vision-language models.<n>Our method is VLM-agnostic, requires no fine-tuning, demonstrates robustness to distribution shift, and works effectively with as few as 10 training images per class.
  arXiv  Detail & Related papers  (2025-11-27T01:48:27Z)
• Enhancing Zero-Shot Vision Models by Label-Free Prompt Distribution Learning and Bias Correcting [55.361337202198925]
  Vision-language models, such as CLIP, have shown impressive generalization capacities when using appropriate text descriptions. We propose a label-Free prompt distribution learning and bias correction framework, dubbed as **Frolic**, which boosts zero-shot performance without the need for labeled data.
  arXiv  Detail & Related papers  (2024-10-25T04:00:45Z)
• Guided Score identity Distillation for Data-Free One-Step Text-to-Image Generation [62.30570286073223]
  Diffusion-based text-to-image generation models have demonstrated the ability to produce images aligned with textual descriptions.<n>We introduce a data-free guided distillation method that enables the efficient distillation of pretrained Diffusion models without access to the real training data.<n>By exclusively training with synthetic images generated by its one-step generator, our data-free distillation method rapidly improves FID and CLIP scores, achieving state-of-the-art FID performance while maintaining a competitive CLIP score.
  arXiv  Detail & Related papers  (2024-06-03T17:44:11Z)
• Individual Fairness Through Reweighting and Tuning [0.23395944472515745]
  Inherent bias within society can be amplified and perpetuated by artificial intelligence (AI) systems. Recently, Graph Laplacian Regularizer (GLR) has been used as a substitute for the common Lipschitz condition to enhance individual fairness. In this work, we investigated whether defining a GLR independently on the train and target data could maintain similar accuracy.
  arXiv  Detail & Related papers  (2024-05-02T20:15:25Z)
• Forgery-aware Adaptive Transformer for Generalizable Synthetic Image Detection [106.39544368711427]
  We study the problem of generalizable synthetic image detection, aiming to detect forgery images from diverse generative methods. We present a novel forgery-aware adaptive transformer approach, namely FatFormer. Our approach tuned on 4-class ProGAN data attains an average of 98% accuracy to unseen GANs, and surprisingly generalizes to unseen diffusion models with 95% accuracy.
  arXiv  Detail & Related papers  (2023-12-27T17:36:32Z)
• Consistency Regularization for Generalizable Source-free Domain Adaptation [62.654883736925456]
  Source-free domain adaptation (SFDA) aims to adapt a well-trained source model to an unlabelled target domain without accessing the source dataset. Existing SFDA methods ONLY assess their adapted models on the target training set, neglecting the data from unseen but identically distributed testing sets. We propose a consistency regularization framework to develop a more generalizable SFDA method.
  arXiv  Detail & Related papers  (2023-08-03T07:45:53Z)
• Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction [49.15931834209624]
  We present the first benchmark that simulates the evaluation of open information extraction models in the real world.<n>We design and annotate a large-scale testbed in which each example is a knowledge-invariant clique.<n>By further elaborating the robustness metric, a model is judged to be robust if its performance is consistently accurate on the overall cliques.
  arXiv  Detail & Related papers  (2023-05-23T12:05:09Z)
• Leveraging Unlabeled Data to Predict Out-of-Distribution Performance [63.740181251997306]
  Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions. In this work, we investigate methods for predicting the target domain accuracy using only labeled source data and unlabeled target data. We propose Average Thresholded Confidence (ATC), a practical method that learns a threshold on the model's confidence, predicting accuracy as the fraction of unlabeled examples.
  arXiv  Detail & Related papers  (2022-01-11T23:01:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.