Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

Deep Learning models have shown remarkable performance in a broad range of vision
tasks. However, they are often vulnerable against domain shifts at test-time. Test-time …

Video Moment Retrieval (VMR) targets to retrieve the specific moment corresponding to a
sentence query from an untrimmed video. Although recent respectable works have made …

Real-world systems often encounter new data over time which leads to experiencing target
domain shifts. Existing Test-Time Adaptation (TTA) methods tend to apply computationally …

Abstract Fully Test-Time Adaptation (TTA), which aims at adapting models to data drifts, has
recently attracted wide interest. Numerous tricks and techniques have been proposed to …

Despite their exceptional performance in vision tasks deep learning models often struggle
when faced with domain shifts during testing. Test-Time Training (TTT) methods have …

Machine learning algorithms have achieved remarkable success across various disciplines,
use cases and applications, under the prevailing assumption that training and test samples …

Test-time adaptation (TTA) adapts the pre-trained models to test distributions during the
inference phase exclusively employing unlabeled test data streams, which holds great value …

Pre-trained vision-language models (VLMs), exemplified by CLIP, demonstrate remarkable
adaptability across zero-shot classification tasks without additional training. However, their …

In this paper, we study the application of Test-Time Training (TTT) as a solution to handling
distribution shifts in speech applications. In particular, we introduce distribution-shifts to the …