Visual text rendering poses a fundamental challenge for contemporary text-to-image
generation models, with the core problem lying in text encoder deficiencies. To achieve …

Contrastive Language-Image Pre-training (CLIP) has been a celebrated method for training
vision encoders to generate image/text representations facilitating various applications …

Multi-modality pre-training paradigm that aligns protein sequences and biological
descriptions has learned general protein representations and achieved promising …

Does seeing always mean knowing? Large Vision-Language Models (LVLMs) integrate
separately pre-trained vision and language components, often using CLIP-ViT as vision …

In this paper, we propose a new method to enhance compositional understanding in pre-
trained vision and language models (VLMs) without sacrificing performance in zero-shot …

A key property of reasoning systems is the ability to make sharp decisions on their input
data. For contemporary AI systems, a key carrier of sharp behaviour is the softmax function …

Camouflaged Object Segmentation (COS) faces significant challenges due to the scarcity of
annotated data, where meticulous pixel-level annotation is both labor-intensive and costly …

Recent contrastive multimodal vision-language models like CLIP have demonstrated robust
open-world semantic understanding, becoming the standard image backbones for vision …

Background Pneumoconiosis staging is challenging due to the low clarity of X-ray images
and the small, diffuse nature of the lesions. Additionally, the scarcity of annotated data …

Vision-language models have demonstrated impressive capabilities in associating images
and text by pretraining on extensive image-text paired data. The paradigm of continual …