EdgeVidCap:IoT エッジ カメラ用のチャネル・スペース・デュアル・ブランチ・ライトウェイト・ビデオ・キャプション・モデル
Lan Guo1, Xuyang Li1, Jinqiang Wang1
1School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.
Sensors (Basel, Switzerland)
|August 28, 2025
まとめ
この研究は,IoTエッジカメラ用の軽量なビデオキャプションモデルであるEdgeVidCapを紹介しています. 効率的なビデオ理解と正確な記述生成をリソース制限のあるデバイスで実現します.
科学分野:
- 人工知能
- コンピュータ・ビジョン
- モノのインターネット
背景:
- エッジ・コンピューティングとIoTの統合を備えたインテリジェント・エッジ・カメラは ローカルビデオの理解を可能にします
- 既存のビデオキャプションモデルは計算が密集しており,リソースが限られたIoTデバイスでの展開を妨げています.
研究 の 目的:
- IoT エッジカメラの効率的なリアルタイム処理のための軽量なビデオキャプションモデル,EdgeVidCapを開発する.
- 現在のビデオ字幕化ソリューションにおける高い計算複雑さと大きなパラメータ数の制限に対処するためです.
主な方法:
- 効率的な時空特征モデリングのために,チャネルの注意とステート・スペース・モデル (SSM) を組み合わせたシナジェティック・アテンション・ステート・マンバ (SASM) モジュールを提案した.
- ダイナミックな機能加重で自動リグレッシブなキャプション生成のための適応的注意誘導のLSTMデコーダーを開発した.
- 処理効率を高めるため,簡素化されたフレームフィルタリングメカニズムを実装しました.
主要な成果:
- EdgeVidCapは,MSR-VTTとMSVDのデータセットにおける既存のビデオキャプション方法と比較して,精度が向上したことを示した.
- このモデルは,フレームのフィルタリングを簡素化したため,より高い処理効率を達成しました.
- フレーム選択の後により信頼性の高いテキスト記述を生成します.
結論:
- EdgeVidCapは,IoTのエッジデバイスで軽量なビデオキャプションのための効果的なソリューションを提供します.
- 提案されているSASMモジュールとアダプティブLSTMデコーダーは,効率的で正確なビデオ理解に貢献します.
- このモデルは,リソースが制限されているエッジコンピューティング環境のリアルタイム処理要件を満たしています.
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