Visual prompt engineering is a fundamental methodology in the field of visual and image
artificial general intelligence. As the development of large vision models progresses, the …

Contrastive Language-Image Pre-training (CLIP), a simple yet effective pre-training
paradigm, successfully introduces text supervision to vision models. It has shown promising …

Abstract Contrastive Language-Image Pre-training (CLIP) has been the cornerstone for zero-
shot classification, text-image retrieval, and text-image generation by aligning image and …

Transformer is a promising neural network learner, and has achieved great success in
various machine learning tasks. Thanks to the recent prevalence of multimodal applications …

We aim at advancing blind image quality assessment (BIQA), which predicts the human
perception of image quality without any reference information. We develop a general and …

We introduce MotionCLIP, a 3D human motion auto-encoder featuring a latent embedding
that is disentangled, well behaved, and supports highly semantic textual descriptions …

Recent advancements in text-to-image diffusion models have yielded impressive results in
generating realistic and diverse images. However, these models still struggle with complex …

Generative AI models have shown impressive ability to produce images with text prompts,
which could benefit creativity in visual art creation and self-expression. However, it is …

Large vision-language models, such as CLIP, learn robust representations of text and
images, facilitating advances in many downstream tasks, including zero-shot classification …

CLIPDraw is an algorithm that synthesizes novel drawings from natural language input. It
does not require any additional training; rather, a pre-trained CLIP language-image encoder …