大規模なマルチモダルモデルのための次なるトークンの予測を備えたマルチモダル学習
Xinlong Wang1, Yufeng Cui2, Jinsheng Wang2
1Beijing Academy of Artificial Intelligence (BAAI), Beijing, China. xinlong.wang96@gmail.com.
Nature
|January 28, 2026
まとめ
Emu3は,新しいマルチモダルモデルで,テキスト,画像,ビデオタスクの次のトークン予測を使用しています. この統一されたアプローチは,複雑なアーキテクチャのない既存のモデルとマッチし,人工知能を進歩させています.
科学分野:
- 人工知能 (AI) とは,人工知能 (AI) のことです.
- 機械学習 (Machine Learning) とは,機械学習 (Machine Learning) について学ぶことです.
- コンピュータビジョン コンピュータビジョン
背景:
- テキスト,画像,ビデオを統合した多式学習は,AIの重要な課題です.
- 現在のアプローチは,拡散モデルや構成フレームワークなどの特殊なアーキテクチャに依存することが多い.
- 次のトークンの予測には,高度な言語モデルがありますが,マルチモダルのアプリケーションは限られています.
研究 の 目的:
- マルチモダルモデルの新しいファミリーであるEmu3を紹介します.
- 次のトークンの予測のみを使用して,マルチモダルの学習に統一されたアプローチを実証します.
- 多様なマルチモダルのタスクで最先端のパフォーマンスを達成するために.
主な方法:
- Emu3のモデルは,次なるトークンの予測を用いてのみ訓練された.
- モデルは,複数のモダリティで知覚と生成のタスクで評価されました.
- 特定のアプリケーションには,ビデオ生成とビジョン・ランゲージ・アクション・モデリングが含まれています.
主要な成果:
- Emu3は,タスク固有のモデルやフラッグシップシステムに匹敵するパフォーマンスを達成した.
- このモデルは,高精度ビデオ生成能力を実証した.
- Emu3は,交差した視覚言語生成とロボット操作のタスクを成功裏に実行しました.
結論:
- 統一されたマルチモダルの学習は,次なるトークンの予測を通じて達成可能である.
- Emu3は,大規模なマルチモダルAIのための堅牢な基盤を提供します.
- このアプローチは,より一般的で統一されたマルチモダルインテリジェンスへの道を開きます.
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