This study presents a novel framework called Domain-Aware Prompt learning (DAP) to help autonomous agents navigate in unseen environments using language instructions. The study highlights the challenge that most pretrained…
Continue readingThe article introduces two intelligibility prediction systems derived from a noise-robust automatic speech recognition model. These systems are designed to predict speech intelligibility in noisy environments, which is crucial for…
Continue readingThe study investigates the potential of transferring ‘complementary’ knowledge from one pretrained model to another without performance degradation. This task is complex as additional knowledge can be contained in stronger,…
Continue readingThe research paper investigates the possibility of transferring ‘complementary’ knowledge between any arbitrary pairing of pretrained models, without any performance degradation. The authors found that each model learns unique feature…
Continue readingThis article investigates the unwanted bias in pretrained vision-and-language (V&L) models, with a focus on gender bias. The authors quantified bias amplification in pretraining and after fine-tuning on three families…
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