FIESTA: Fisher Information-based Efficient Selective Test-time Adaptation

• URL: http://arxiv.org/abs/2503.23257v1
• Date: Sat, 29 Mar 2025 23:56:32 GMT
• Title: FIESTA: Fisher Information-based Efficient Selective Test-time Adaptation
• Authors: Mohammadmahdi Honarmand, Onur Cezmi Mutlu, Parnian Azizian, Saimourya Surabhi, Dennis P. Wall,
• Abstract summary: This paper introduces a novel Fisher-driven selective adaptation framework that dynamically identifies and updates only the most critical model parameters.<n> Experiments on the challenging AffWild2 benchmark demonstrate that our approach significantly outperforms existing TTA methods.<n>The proposed approach not only enhances recognition accuracy but also dramatically reduces computational overhead, making test-time adaptation more practical for real-world affective computing applications.
• Score: 2.876586838098149
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robust facial expression recognition in unconstrained, "in-the-wild" environments remains challenging due to significant domain shifts between training and testing distributions. Test-time adaptation (TTA) offers a promising solution by adapting pre-trained models during inference without requiring labeled test data. However, existing TTA approaches typically rely on manually selecting which parameters to update, potentially leading to suboptimal adaptation and high computational costs. This paper introduces a novel Fisher-driven selective adaptation framework that dynamically identifies and updates only the most critical model parameters based on their importance as quantified by Fisher information. By integrating this principled parameter selection approach with temporal consistency constraints, our method enables efficient and effective adaptation specifically tailored for video-based facial expression recognition. Experiments on the challenging AffWild2 benchmark demonstrate that our approach significantly outperforms existing TTA methods, achieving a 7.7% improvement in F1 score over the base model while adapting only 22,000 parameters-more than 20 times fewer than comparable methods. Our ablation studies further reveal that parameter importance can be effectively estimated from minimal data, with sampling just 1-3 frames sufficient for substantial performance gains. The proposed approach not only enhances recognition accuracy but also dramatically reduces computational overhead, making test-time adaptation more practical for real-world affective computing applications.

Related papers

• Optimal Transport-Guided Source-Free Adaptation for Face Anti-Spoofing [58.56017169759816]
  We introduce a novel method in which the face anti-spoofing model can be adapted by the client itself to a target domain at test time.<n>Specifically, we develop a prototype-based base model and an optimal transport-guided adaptor.<n>In cross-domain and cross-attack settings, compared with recent methods, our method achieves average relative improvements of 19.17% in HTER and 8.58% in AUC.
  arXiv  Detail & Related papers  (2025-03-29T06:10:34Z)
• Forecast-PEFT: Parameter-Efficient Fine-Tuning for Pre-trained Motion Forecasting Models [68.23649978697027]
  Forecast-PEFT is a fine-tuning strategy that freezes the majority of the model's parameters, focusing adjustments on newly introduced prompts and adapters. Our experiments show that Forecast-PEFT outperforms traditional full fine-tuning methods in motion prediction tasks. Forecast-FT further improves prediction performance, evidencing up to a 9.6% enhancement over conventional baseline methods.
  arXiv  Detail & Related papers  (2024-07-28T19:18:59Z)
• Parameter-Efficient Fine-Tuning With Adapters [5.948206235442328]
  This research introduces a novel adaptation method utilizing the UniPELT framework as a base. Our method employs adapters, which enable efficient transfer of pretrained models to new tasks with minimal retraining of the base model parameters.
  arXiv  Detail & Related papers  (2024-05-09T01:40:38Z)
• AR-TTA: A Simple Method for Real-World Continual Test-Time Adaptation [1.4530711901349282]
  We propose to validate test-time adaptation methods using datasets for autonomous driving, namely CLAD-C and SHIFT. We observe that current test-time adaptation methods struggle to effectively handle varying degrees of domain shift. We enhance the well-established self-training framework by incorporating a small memory buffer to increase model stability.
  arXiv  Detail & Related papers  (2023-09-18T19:34:23Z)
• REALM: Robust Entropy Adaptive Loss Minimization for Improved Single-Sample Test-Time Adaptation [5.749155230209001]
  Fully-test-time adaptation (F-TTA) can mitigate performance loss due to distribution shifts between train and test data. We present a general framework for improving robustness of F-TTA to noisy samples, inspired by self-paced learning and robust loss functions.
  arXiv  Detail & Related papers  (2023-09-07T18:44:58Z)
• Visual Prompt Tuning for Test-time Domain Adaptation [48.16620171809511]
  We propose a simple recipe called data-efficient prompt tuning (DePT) with two key ingredients. We find such parameter-efficient finetuning can efficiently adapt the model representation to the target domain without overfitting to the noise in the learning objective. With much fewer parameters, DePT demonstrates not only state-of-the-art performance on major adaptation benchmarks, but also superior data efficiency.
  arXiv  Detail & Related papers  (2022-10-10T16:45:13Z)
• CAFA: Class-Aware Feature Alignment for Test-Time Adaptation [50.26963784271912]
  Test-time adaptation (TTA) aims to address this challenge by adapting a model to unlabeled data at test time. We propose a simple yet effective feature alignment loss, termed as Class-Aware Feature Alignment (CAFA), which simultaneously encourages a model to learn target representations in a class-discriminative manner.
  arXiv  Detail & Related papers  (2022-06-01T03:02:07Z)
• Efficient Test-Time Model Adaptation without Forgetting [60.36499845014649]
  Test-time adaptation seeks to tackle potential distribution shifts between training and testing data. We propose an active sample selection criterion to identify reliable and non-redundant samples. We also introduce a Fisher regularizer to constrain important model parameters from drastic changes.
  arXiv  Detail & Related papers  (2022-04-06T06:39:40Z)
• Listen, Adapt, Better WER: Source-free Single-utterance Test-time Adaptation for Automatic Speech Recognition [65.84978547406753]
  Test-time Adaptation aims to adapt the model trained on source domains to yield better predictions for test samples. Single-Utterance Test-time Adaptation (SUTA) is the first TTA study in speech area to our best knowledge.
  arXiv  Detail & Related papers  (2022-03-27T06:38:39Z)
• Parameter-free Online Test-time Adaptation [19.279048049267388]
  We show how test-time adaptation methods fare for a number of pre-trained models on a variety of real-world scenarios. We propose a particularly "conservative" approach, which addresses the problem with a Laplacian Adjusted Maximum Estimation (LAME) Our approach exhibits a much higher average accuracy across scenarios than existing methods, while being notably faster and have a much lower memory footprint.
  arXiv  Detail & Related papers  (2022-01-15T00:29:16Z)

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.