Summary of Anomalycontrol: Learning Cross-modal Semantic Features For Controllable Anomaly Synthesis, by Shidan He et al.
AnomalyControl: Learning Cross-modal Semantic Features for Controllable Anomaly Synthesis
by Shidan He, Lei Liu, Shen Zhao
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel framework called AnomalyControl for text-to-image anomaly synthesis, which learns cross-modal semantic features as guidance signals to improve the realism and generalization of generated abnormal samples. The framework adopts a flexible prompt pair comprising a text-image reference prompt and a targeted text prompt, with a Cross-modal Semantic Modeling (CSM) module extracting cross-modal semantic features from textual and visual descriptors. An Anomaly-Semantic Enhanced Attention (ASEA) mechanism is then formulated to focus on specific visual patterns of the anomaly, enhancing realism and contextual relevance. A Semantic Guided Adapter (SGA) encodes effective guidance signals for a controllable synthesis process. Experimental results demonstrate state-of-the-art performance in anomaly synthesis compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us create better fake images of things that don’t usually happen. Currently, we can only make simple and unrealistic images. The new method uses words and pictures together to learn what makes unusual things look like. It’s like a special filter that makes the image more realistic. This is important because it will help computers detect things that are unusual or abnormal, which is useful in many areas like medicine, finance, and security. |
Keywords
» Artificial intelligence » Attention » Generalization » Prompt
