使用 CLIPSeg 進行零樣本（Zero-shot）圖像分割
Original: Zero-shot image segmentation with CLIPSeg
This article introduces CLIPSeg, an innovative architecture presented at CVPR 2022, designed to solve the problem of traditional image…
Hugging Face 介紹了 CLIPSeg 模型，這是一個基於 CLIP 的零樣本圖像分割工具。使用者只需輸入簡單的文字提示（如「貓」或「杯子」）或參考影像，模型就能精確分割出目標物體。此技術免去了傳統分割模型需要大量標記資料與重新訓練的痛點，並已整合至 Hugging Face transformers 庫中，開發者只需幾行程式碼即可輕鬆上手。
This article introduces CLIPSeg, an innovative architecture presented at CVPR 2022, designed to solve the problem of traditional image segmentation models being limited to specific trained categories. CLIPSeg cleverly combines OpenAI's CLIP model with a lightweight decoder. CLIP itself possesses powerful cross-modal understanding, capable of projecting text and images into the same vector space. CLIPSeg builds on CLIP's feature extractor and adds a decoder to produce binary segmentation masks.
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