Summary of Afanet: Adaptive Frequency-aware Network For Weakly-supervised Few-shot Semantic Segmentation, by Jiaqi Ma et al.
AFANet: Adaptive Frequency-Aware Network for Weakly-Supervised Few-Shot Semantic Segmentation
by Jiaqi Ma, Guo-Sen Xie, Fang Zhao, Zechao Li
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an adaptive frequency-aware network (AFANet) for weakly-supervised few-shot semantic segmentation (WFSS), which is a challenging visually intensive task. To address the issue of pixel-level annotations being time-consuming and costly, AFANet utilizes image-level annotations. The approach involves two main components: a cross-granularity frequency-aware module (CFM) that decouples RGB images into high-frequency and low-frequency distributions and optimizes semantic structural information, and a CLIP-guided spatial-adapter module (CSM) that performs spatial domain adaptive transformation on textual information through online learning. The CSM provides enriched cross-modal semantic information for CFM. The paper’s performance is evaluated on the Pascal-5i and COCO-20i datasets, achieving state-of-the-art results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AFANet is a new way to do image segmentation with just a few examples. Instead of needing lots of labeled pictures, it uses text from a model like CLIP to help figure out what’s in an image. This makes it much faster and cheaper than before. The paper shows that AFANet works well on big datasets like Pascal-5i and COCO-20i. |
Keywords
» Artificial intelligence » Few shot » Image segmentation » Online learning » Semantic segmentation » Supervised
