Comparison of InternVideo2 and VideoMAE-V2 Video Models

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Introduction

Purpose: InternVideo2 and VideoMAE-V2 are video foundation models designed for video understanding tasks.
Performance: InternVideo2 achieves state-of-the-art performance in video recognition and video-text tasks.
Architecture: VideoMAE-V2 utilizes a video masked autoencoder approach with a dual masking design.
Training: InternVideo2 uses different configurations and resources across training stages.
Innovation: VideoMAE-V2 introduces innovative dual masking, differentiating it from its predecessor, VideoMAE V1.

InternVideo2: Designed for video understanding tasks including video recognition and video-text tasks.
VideoMAE-V2: Intended as a scalable pre-trainer for building video foundation models.

InternVideo2: Achieves state-of-the-art results in action recognition, video-text tasks.
VideoMAE-V2: Effective as a scalable and general self-supervised pre-trainer.

InternVideo2: Focuses on scaling video foundation models for multimodal understanding.
VideoMAE-V2: Builds on masked autoencoder with a novel dual masking approach.

InternVideo2: Utilizes varying configurations and resources across different training stages.
VideoMAE-V2: Employs dual masking to optimize training without reconstructing the full video clip.

InternVideo2: Focuses on achieving new benchmarks for multimodal video understanding tasks.
VideoMAE-V2: Introduces a dual masking design, setting it apart from previous versions.