用预训练几何先验提升视频物体中心表征效率
Efficient Object-centric Representation Learning with Pre-trained Geometric Prior
- 基于预训练视觉模型构建弱监督框架，强化几何理解
- 在复杂合成视频上达到与有监督方法相当的性能
- 无需深度信息即可高效表征多物体场景，适合真实世界应用
本文针对视频物体中心表征学习中的关键挑战提出新方法。现有方法在复杂场景下表现不佳，本文提出一种新颖的弱监督框架，强调几何理解并利用预训练视觉模型增强物体发现能力。所提方法设计了高效的槽解码器，专为物体中心学习优化，可在不依赖显式深度信息的情况下有效表征多物体场景。在合成视频基准测试中，随着物体数量、运动复杂度、遮挡和相机运动增加，该方法在保持计算效率的同时，达到与有监督方法相当的性能，推动该领域向更实用的复杂现实场景应用发展。
原文摘要 · Abstract (English)
This paper addresses key challenges in object-centric representation learning of video. While existing approaches struggle with complex scenes, we propose a novel weakly-supervised framework that emphasises geometric understanding and leverages pre-trained vision models to enhance object discovery. Our method introduces an efficient slot decoder specifically designed for object-centric learning, enabling effective representation of multi-object scenes without requiring explicit depth information. Results on synthetic video benchmarks with increasing complexity in terms of objects and their movement, object occlusion and camera motion demonstrate that our approach achieves comparable performance to supervised methods while maintaining computational efficiency. This advances the field towards more practical applications in complex real-world scenarios.
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