Alpha-CLIP: A CLIP Model Focusing on Wherever You Want
This addresses the need for more targeted image analysis and editing in tasks like open-world recognition and generation, representing an incremental improvement over CLIP.
The paper tackles the problem of CLIP models lacking focus on specific image regions for finer understanding and controlled editing, by introducing Alpha-CLIP with an auxiliary alpha channel trained on millions of RGBA region-text pairs, which preserves CLIP's recognition ability and enables precise content emphasis control.
Contrastive Language-Image Pre-training (CLIP) plays an essential role in extracting valuable content information from images across diverse tasks. It aligns textual and visual modalities to comprehend the entire image, including all the details, even those irrelevant to specific tasks. However, for a finer understanding and controlled editing of images, it becomes crucial to focus on specific regions of interest, which can be indicated as points, masks, or boxes by humans or perception models. To fulfill the requirements, we introduce Alpha-CLIP, an enhanced version of CLIP with an auxiliary alpha channel to suggest attentive regions and fine-tuned with constructed millions of RGBA region-text pairs. Alpha-CLIP not only preserves the visual recognition ability of CLIP but also enables precise control over the emphasis of image contents. It demonstrates effectiveness in various tasks, including but not limited to open-world recognition, multimodal large language models, and conditional 2D / 3D generation. It has a strong potential to serve as a versatile tool for image-related tasks.