3 accepted papers
Positional bias—where models overemphasize certain positions regardless of content—has been shown to negatively impact model performance across various tasks. While recent research has extensively examined positional bias in text generation models, its presence and effects in representation models r
Vision-Language Models (VLMs) bridge the gap between visual and textual data, enabling multimodal tasks like Visual Question Answering (VQA). Leveraging this capability, Medical VQA systems have the potential to transform clinical decision-making by allowing healthcare providers to query medical ima…
As a pioneering vision-language model, CLIP (Contrastive Language-Image Pre-training) has achieved significant success across various domains and a wide range of downstream vision-language tasks. However, the text encoders in popular CLIP models are limited to processing only 77 text tokens, which c…