Object segmentation is a crucial problem in computer vision, used in various applications like autonomous driving, surveillance, and robotics. Its purpose is to accurately identify and label the boundaries of objects in an image. This allows for a clear visual representation of each object in the image.
With the recent advancements in deep learning, object segmentation has become relatively easier to solve. However, challenging scenarios still exist, and researchers continue to develop sophisticated algorithms to address these issues.
One of the main challenges in object segmentation models is their limited dictionaries. Most existing models can only segment objects that they have been trained on. For example, if a model has only been trained on images of cats and dogs, it won’t be able to segment a panda in an image.
There have been attempts to overcome this limitation and create a unified framework for parsing all object instances and scene semantics simultaneously. However, few works have successfully achieved this goal.
Many current approaches rely on large-scale text-image discriminative models for open-vocabulary recognition. While these models are good at classifying individual object proposals or pixels, they lack spatial and relational understanding necessary for open-vocabulary panoptic segmentation.
To address this issue, a new approach called ODISE (Open-vocabulary DIffusion-based panoptic SEgmentation) has been proposed. ODISE leverages both large-scale diffusion models and text-image discriminative models. By inputting an image and its caption into a pre-trained frozen text-to-image diffusion model, ODISE extracts internal features that are then used by the mask generator to produce panoptic masks of all possible concepts in the image. The mask classification module categorizes each mask into open-vocabulary categories by associating each predicted mask’s diffusion features with text embeddings of several object category names. ODISE performs open-vocabulary panoptic inference using both the text-image diffusion and discriminative models to classify a predicted mask.
ODISE is the first work to explore large-scale text-to-image diffusion models for open-vocabulary segmentation tasks. It offers a novel pipeline that effectively combines text-image diffusion and discriminative models to achieve open-vocabulary panoptic segmentation. ODISE outperforms existing baselines in various open-vocabulary recognition tasks, significantly advancing the field.
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