This tier is broken into the following sub-tiers:Ĭharacters who demonstrate power equivalent to destroying/creating a 0-D level construct of any size, or 3 level of infinity/degrees of reality/fiction transcendence or similar beneath a 3-D reality. Please note that existing as a drawing or being made of data / information is not to be ranked at this tier, as such beings are still 3-dimensional, but in an incredibly small scale. This tier pertains to characters who can create/destroy or affect the whole structure of a lower-dimensional universe, or lower layers/levels of reality. See this page for more information.įurthermore, the higher bounds of the system make use of infinite cardinal numbers as a metric to accurately quantify and categorize meta-hierarchies beyond even infinitely-layered ones, and as such, it is advisable to read this explanation as well. However, higher-dimensionality is indeed a valid way to jump unto higher tiers if the higher-dimensional being / object in question is either treated as being infinitely and/or qualitatively above lower-dimensional ones specifically because of their dimensionality, or is provably infinite, in which case it is equated to the size of the entire n-dimensional real coordinate space in which it resides. Hence, they must either be placed at Unknown or simply reasonably scale relative to their best feats, provided they are not outliers or something of the sort, of course. Hence, being far stronger than a character that belongs to a certain tier does not necessarily qualify one for a higher rating.Īs of now, we do not consider higher-dimensional constructs as being necessarily infinitely greater than lower-dimensional equivalents until further context as to their nature and size is provided by a work of fiction. It is also important to know that the difference between the lowest and highest bounds of a given tier are extremely variable, and can be absolutely massive in scale. For instance, harming a character with a certain level of Durability also allows another character to qualify for the corresponding tier.įurthermore, it should be noted that characters from a higher tier are not necessarily invincible to entities of lower tiers, as certain powers and abilities can potentially bypass the difference in strength entirely, allowing the latter to contend with, or overpower such characters. Of characters.The following is a comprehensive overview of the hierarchical system which this wiki utilizes in order to properly categorize and index fictional characters and entities based on the scale of their feats, and the varying scopes which they can affect or create/destroy, although it should always be kept in mind that, while Destructive Capacity and Area of Effect are some of the most primary ways to qualify for a certain tier, they are not the only ones. Through the experiments, we demonstrate thatĬAFE-Net improves the STR performance on languages containing numerous number Respectively, we propose a novel confidence ensemble method to compensate the By training twoĮxperts to focus on learning contextual and visual representations, Utilizing a dataset with a balanced number of characters. Long-tailed dataset composed of common words used in everyday life and 2)Ĭontext-free expert focuses on correctly predicting individual characters by Novel Context-Aware and Free Experts Network (CAFE-Net) using two experts: 1)Ĭontext-aware expert learns the contextual representation trained with a Training with such a synthetic dataset interferes the model with learning theĬontextual information (i.e., relation among characters), which is also While increasing a substantial number of tail classes without considering theĬontext helps the model to correctly recognize characters individually, To address such an issue, weĬonducted an empirical analysis using synthetic datasets with differentĬharacter-level distributions (e.g., balanced and long-tailed distributions). However, STR models show a large performanceĭegradation on languages with a numerous number of characters (e.g., ChineseĪnd Korean), especially on characters that rarely appear due to the long-tailedĭistribution of characters in such languages. The majority of the studies focused mainly on the English language, which only Download a PDF of the paper titled Improving Scene Text Recognition for Character-Level Long-Tailed Distribution, by Sunghyun Park and 3 other authors Download PDF Abstract: Despite the recent remarkable improvements in scene text recognition (STR),
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