3 accepted papers
Test-time prompt tuning (TPT) has emerged as a promising technique for enhancing the adaptability of vision-language models by optimizing textual prompts using unlabeled test data. However, prior studies have observed that TPT often produces poorly calibrated models, raising concerns about the relia
When continual test-time adaptation (TTA) persists over the long term, errors accumulate in a model and further lead it to predict only a few classes regardless of the input, known as model collapse. Recent studies have explored reset strategies that erase these accumulated errors completely. Howeve…
Recent advances in computer vision have made training object detectors more efficient and effective; however, assessing their performance in real-world applications still relies on costly manual annotation. To address this limitation, we develop an automated model evaluation (AutoEval) framework for…