CLIPを使用した可視赤外線画像融合のための言語駆動クロス注意
Xue Wang1, Jiatong Wu1, Pengfei Zhang1
1Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.
Sensors (Basel, Switzerland)
|August 28, 2025
まとめ
この研究は,新しいテキスト誘導赤外線と可視画像融合ネットワークを導入します. このモデルは,困難な環境におけるロボットのマルチモダルの知覚を向上させ,ナビゲーションと環境の理解を向上させます.
科学分野:
- コンピュータ・ビジョン
- 人工知能
- ロボット
背景:
- シングルモダリティー画像は 低光環境や複雑な環境では困難です
- 多様性融合 (可視と赤外線) はロボットの知覚に不可欠です
- シーンの理解が強化されれば,ナビゲーション,ローカライゼーション,環境認識が得られます.
研究 の 目的:
- テキストガイドによる赤外線と可視画像融合ネットワークを開発する.
- セマンティック情報を統合することにより,マルチモダルの融合の有効性を向上させる.
- 視覚的な詳細と意味の理解を高める高品質の融合画像を生成します.
主な方法:
- 画像融合とテキストガイドモジュールを組み合わせた 新しいフレームワークです
- マルチモダルの機能を統合するための領域間注意力メカニズム
- テキスト記述から意味のヒントを抽出して融合を導くためのCLIPモデル.
主要な成果:
- LLVIPとTNOのベンチマークデータセットで強力な量的なパフォーマンスを達成しました.
- マルチモダルの知覚のタスクで堅牢でスケーラブルなパフォーマンスを示した.
- 質の高い色合いの画像を生成し 意味的理解を豊かにする.
結論:
- 提案されているテキスト・ガイデッド・フュージョン・ネットワークは,視覚情報と言語情報を効果的に統合しています.
- このフレームワークは,現実世界のマルチモダルの知覚アプリケーションに有望なソリューションを提供します.
- 強化された融合能力は 複雑な環境における 自動運転システムにとって 極めて重要です
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