TextVerifier formalizes text rendering verification as a target-conditioned task: determining whether an image crop is a correct rendering of a given character. The model uses a character's Ideographic Description Sequence (IDS) instead of a single character and is trained on 12.7M crop-IDS pairs. Evaluation shows high performance compared to OCR and other models.
The chart will appear after repeat observations. The current metric comes from the source.
2 source votesReal observations only. History before source connection is not reconstructed.
Can be useful for verifying the quality of Chinese text rendering in generative models, identifying hidden defects that other systems miss.
Determining whether an image is AI-generated does not establish whether the situation it depicts is factually supported. We formulate Image Factuality Assessment: given an image and a generic verification instruction, a system returns a supported or refuted verdict with an evidence-grounded explanation. We introduce EviLens, an expert-reviewed benchmark of 10,174 supported and refuted images covering twelve factual error types.
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