Test-time adaptation methods assume privileged access to model internals: parameters for fine-tuning, statistics for recalibration, or architectural components for modification. This assumption fails when models are deployed as certified systems, encrypted services, or under regulatory constraints that prohibit parameter changes. We present ITEM (Input Transformation via Entropy Minimization), the first preparation-agnostic sample adaptation method for test-time adaptation. ITEM learns input transformations that minimize prediction entropy using only gradient signals through frozen models, exploiting the principle that well-calibrated models produce low-entropy outputs on familiar data. Unlike existing sample adaptation methods that require specialized training procedures or parameter updates, ITEM works with any pre-trained model without modification or preparation requirements. Using scalar transformations as proof of concept, we demonstrate adaptation under extreme data scarcity: models trained on 10 samples per class and adapted with single calibration samples. ITEM significantly reduces performance degradation from corruption while existing methods fail or show negligible improvement. Our results establish that effective test-time adaptation is possible without model modification, architectural knowledge, or training preparation, opening new possibilities for adapting deployed models under real-world constraints.

This paper presents an analysis of the data and compute efficiency of the TemporalMaxer deep learning model in the context of temporal action localization (TAL), which involves accurately detecting the start and end times of specific video actions. The study explores the performance and scalability of the TemporalMaxer model under limited resources and data availability, focusing on factors such as hardware requirements, training time, and data utilization, thus contributing to the advancement of efficient deep learning models for real-world video tasks. Through a literature review of temporal action recognition models, evaluation of learning curves for data efficiency, and development of metrics to assess the compute efficiency, the study provides insights into the performance trade-offs of the TemporalMaxer model. Experiments conducted on the widely used THUMOS dataset further demonstrate the model's generalizability with limited data, achieving significant accuracy performance with only 50% of the training data. Notably, TemporalMaxer exhibits superior compute efficiency by significantly reducing the number of Multiply-Accumulate operations (MACs) compared to other state-of-the-art models. However, alternative models like TriDet and TadTR outperform TemporalMaxer in training time-constrained scenarios. These findings shed light on the model's practical applicability in resource-constrained environments, offering insights for further optimization and study.