Knowledge Distillation Enhanced Contrastive Masked Autoencoder for Multimodal Representation Learning
Combining contrastive learning, masked data modeling, and knowledge distillation in a novel architecture called KDC-MAE leads to improved multimodal representation learning, outperforming existing methods like CAV-MAE.
LLM2CLIP: 강력한 언어 모델을 활용한 풍부한 시각적 표현 학습
LLM2CLIP은 대규모 언어 모델(LLM)의 강력한 텍스트 이해 능력을 활용하여 CLIP의 시각적 표현 학습 능력을 향상시키는 새로운 접근 방식입니다.
LLM2CLIP：強力な言語モデルで大規模言語モデルの能力を活用した、よりリッチな視覚表現の獲得
LLM2CLIPは、大規模言語モデル（LLM）のテキスト理解能力とオープンワールド知識を活用し、従来のCLIPモデルの視覚表現学習を大幅に向上させる手法である。
LLM2CLIP: Enhancing CLIP's Visual Representation Learning by Integrating Large Language Models
LLM2CLIP leverages the power of large language models (LLMs) to significantly improve the visual representation learning capabilities of CLIP, achieving state-of-the-art performance in various cross-modal tasks.
Capturing Multimodal Interactions Beyond Redundancy: A Contrastive Learning Approach
CoMM, a contrastive multimodal learning strategy, enables the communication between modalities in a single multimodal space, allowing it to capture redundant, unique and synergistic information between modalities.
Zero-shot Cross-Modal Transfer of Reinforcement Learning Policies through a Global Workspace
The author explores the advantages of using a brain-inspired multimodal representation, the Global Workspace, for training RL agents, demonstrating zero-shot cross-modal policy transfer capabilities.
Efficient Vision-Language Pre-training Model: EVE
EVE introduces a unified vision-language model pre-trained with masked signal modeling, achieving state-of-the-art performance on various downstream tasks.
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