×
Well done. You've clicked the tower. This would actually achieve something if you had logged in first. Use the key for that. The name takes you home. This is where all the applicables sit. And you can't apply any changes to my site unless you are logged in.

Our policy is best summarized as "we don't care about _you_, we care about _them_", no emails, so no forgetting your password. You have no rights. It's like you don't even exist. If you publish material, I reserve the right to remove it, or use it myself.

Don't impersonate. Don't name someone involuntarily. You can lose everything if you cross the line, and no, I won't cancel your automatic payments first, so you'll have to do it the hard way. See how serious this sounds? That's how serious you're meant to take these.

×
Register


Required. 150 characters or fewer. Letters, digits and @/./+/-/_ only.
  • Your password can’t be too similar to your other personal information.
  • Your password must contain at least 8 characters.
  • Your password can’t be a commonly used password.
  • Your password can’t be entirely numeric.
Enter the same password as before, for verification.
Login

Grow A Dic
Define A Word
Make Space
Set Task
Mark Post
Apply Votestyle
Create Votes
(From: saved spaces)
Exclude Votes
Apply Dic
Exclude Dic

Automated Testing for Text-to-Image Software

Click here to flash read.

arXiv:2312.12933v2 Announce Type: replace
Abstract: Recently, creative generative artificial intelligence software has emerged as a pivotal assistant, enabling users to generate content and seek inspiration rapidly. Text-to-image (T2I) software, being one of the most widely used among them, is used to synthesize images with simple text input by engaging in a cross-modal process. However, despite substantial advancements in several fields, T2I software often encounters defects and erroneous, including omitting focal entities, low image realism, and mismatched text-image information. The cross-modal nature of T2I software makes it challenging for traditional testing methods to detect defects. Lacking test oracles further increases the complexity of testing. To address this deficiency, we propose ACTesting, an Automated Cross-modal Testing Method of Text-to-Image software, the first testing method designed specifically for T2I software. We construct test samples based on entities and relationship triples following the fundamental principle of maintaining consistency in the semantic information to overcome the cross-modal matching challenges. To address the issue of testing oracle scarcity, we first design the metamorphic relation for T2I software and implement three types of mutation operators guided by adaptability density. In the experiment, we conduct ACTesting on four widely-used T2I software. The results show that ACTesting can generate error-revealing tests, reducing the text-image consistency by up to 20% compared with the baseline. We also conduct the ablation study that effectively showcases the efficacy of each mutation operator, based on the proposed metamorphic relation. The results demonstrate that ACTesting can identify abnormal behaviors of T2I software effectively.

Click here to read this post out
ID: 822637; Unique Viewers: 0
Unique Voters: 0
Total Votes: 0
Votes:
Latest Change: April 26, 2024, 7:31 a.m. Changes:
/u/anonymous
Dictionaries:
Words:
Spaces:
Views: 5
CC:
No creative common's license
Comments: