塗鴉你的關鍵點：基於草圖的少樣本關鍵點檢測

摘要

關鍵點檢測作為現代機器感知的核心技術，在少樣本學習中面臨著挑戰，尤其是在無法獲取與查詢同分佈的源數據時。這一差距通過利用草圖——一種流行的人類表達形式——得到了解決，提供了一種無需源數據的替代方案。然而，掌握跨模態嵌入和處理用戶特定的繪畫風格仍存在挑戰。我們提出的框架通過原型設置，結合基於網格的定位器和原型域適應，克服了這些障礙。通過大量實驗，我們還展示了在跨新穎關鍵點和類別的少樣本收斂方面的成功。

English

Keypoint detection, integral to modern machine perception, faces challenges in few-shot learning, particularly when source data from the same distribution as the query is unavailable. This gap is addressed by leveraging sketches, a popular form of human expression, providing a source-free alternative. However, challenges arise in mastering cross-modal embeddings and handling user-specific sketch styles. Our proposed framework overcomes these hurdles with a prototypical setup, combined with a grid-based locator and prototypical domain adaptation. We also demonstrate success in few-shot convergence across novel keypoints and classes through extensive experiments.

塗鴉你的關鍵點：基於草圖的少樣本關鍵點檢測

Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection

摘要

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