骨格ベースの連続した手話認識のための部分賢明なグラフフーリエ学習
Dong Wei1, Hongxiang Hu1, Gang-Feng Ma1
1College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China.
Journal of imaging
|August 27, 2025
まとめ
この研究では,手話認識のための新しい部分的なグラフフフーリエ学習法が導入され,精度が向上し,計算コストが削減されます. この新しいアプローチは 複雑な身体部位の動きを効果的にモデル化して 視覚言語の理解を向上させます
科学分野:
- コンピュータ科学
- 人工知能
- 人とコンピュータの相互作用
背景:
- 手話認識はRGB入力で計算コストとノイズによる課題に直面しています.
- 非線形時間動力学と 身体の各部位における非同期性を 正確にモデル化することは 難しそうです
研究 の 目的:
- 骨格ベースの連続した手話認識のための新しい部分的なグラフフ・フーリエ学習法 (PGF-SLR) を提案する.
- 身体の各部位の時空関係を均一にモデル化することで,既存の方法の限界を解決する.
主な方法:
- ボディパーツをノードとして,周波数領域の注意をエッジとして扱う部分レベルのフーリエ完全接続グラフを構築した.
- 差別的な行動特性を増幅するために,適応的な周波数増強法を使用した.
- 拡張された理解のために補助的な予測ブランチを備えたダブルブランチアクション学習モジュールを利用しました.
主要な成果:
- PHOENIX14とPHOENIX14- Tのデータセットではそれぞれ3. 31%/ 3. 70%と2. 81%/ 7. 33%の相対的な改善を達成した.
- CSL-Dailyのデータセットで競争力のあるパフォーマンスを示し,強い汎用性を示しています.
- オフラインとオンラインの両方で計算コストを削減します.
結論:
- 提案されたPGF-SLR方法は,サイン言語の認識のための周波数領域の空間時間的依存性を効果的に捉えます.
- PGF-SLRは,継続的な手話認識のための軽量で堅牢で計算効率の高いソリューションを提供します.
- この方法は,正確で迅速な手話の理解を必要とする現実世界のアプリケーションのための大きな可能性を示しています.
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