These custom elements are outlined here or in this paper. Without leaving UMLet, users can thus create and add new element types to their diagrams. An element's look can be modified at run-time by changing a few lines of Java code UMLet then compiles the new element's code on the fly. UMLet also allows users to create their own custom UML elements. Elements can also be modified inside their palettes and immediately used as new templates this way, users can easily tailor UMLet to their modeling needs. Learning about the various element features is supported by prototypically using them from sample palettes. UML elements are modified using text input and a small markdown dialect instead of pop-up dialogs. Relative 30% over the original model and setting a new state-of-the-art.UMLet is a UML tool aimed at providing a fast way of creating UML sketches. Performance, reducing object hallucination rates in MS COCO Captions by a When trainingĭata are available, a learned confidence estimator provides further improved Reasoning in Winoground by a relative 37% and 9%, respectively. Outperforms prior state-of-the-art in image and group scores for compositional Improvement in accuracy by 10% on verb understanding in SVO-Probes and Pretrained models, TLC with algebraic confidence measures achieves a relative Specifically, we fine-tune a vision-language model on imageĬaptioning, input an image and proposed caption to the model, and aggregateĮither algebraic or learned token confidences over words or sequences toĮstimate image-caption consistency. TLC, as a simple yet surprisingly effective method to assess captionĬorrectness. In this work, we explore Token-Level Confidence, or Models often misinterpret the correctness of fine-grained details, leading toĮrrors in outputs such as hallucinating objects in generated captions or poorĬompositional reasoning. Download a PDF of the paper titled Simple Token-Level Confidence Improves Caption Correctness, by Suzanne Petryk and 5 other authors Download PDF Abstract: The ability to judge whether a caption correctly describes an image is aĬritical part of vision-language understanding.
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