从恶性循环到良性循环：用于无监督视频对象中心学习的协同表征学习
From Vicious to Virtuous Cycles: Synergistic Representation Learning for Unsupervised Video Object-Centric Learning
AI总结：
本文提出协同表征学习（SRL）方法，通过编码器与解码器的相互细化打破恶性循环，提升视频对象中心学习的性能。
摘要：
无监督的对象中心学习模型，特别是基于槽位的架构，已在分解复杂场景方面展现出巨大潜力。然而，它们对基于重建的训练的依赖导致了编码器的锐利、高频注意力图与解码器的空间一致但模糊的重建图之间的根本性矛盾。我们发现这种差异导致了恶性循环：编码器产生的噪声特征图迫使解码器在可能性之间平均并产生更模糊的输出，而从模糊重建图计算出的梯度缺乏监督编码器特征所需的高频细节。为了打破这个循环，我们引入了协同表征学习（SRL），建立了一个良性循环，使编码器和解码器相互细化。SRL利用编码器的锐度来在解码器输出中去模糊语义边界，同时利用解码器的空间一致性来去噪编码器的特征。这种相互细化过程通过一个带有槽位正则化目标的预热阶段得到稳定，该阶段最初为每个槽位分配不同的实体。通过弥合编码器和解码器之间的表征差距，SRL在视频对象中心学习基准上实现了最先进的结果。代码可在https://github.com/hynnsk/SRL获取。
英文摘要：
Unsupervised object-centric learning models, particularly slot-based architectures, have shown great promise in decomposing complex scenes. However, their reliance on reconstruction-based training creates a fundamental conflict between the sharp, high-frequency attention maps of the encoder and the spatially consistent but blurry reconstruction maps of the decoder. We identify that this discrepancy gives rise to a vicious cycle: the noisy feature map from the encoder forces the decoder to average over possibilities and produce even blurrier outputs, while the gradient computed from blurry reconstruction maps lacks high-frequency details necessary to supervise encoder features. To break this cycle, we introduce Synergistic Representation Learning (SRL) that establishes a virtuous cycle where the encoder and decoder mutually refine one another. SRL leverages the encoder's sharpness to deblur the semantic boundary within the decoder output, while exploiting the decoder's spatial consistency to denoise the encoder's features. This mutual refinement process is stabilized by a warm-up phase with a slot regularization objective that initially allocates distinct entities per slot. By bridging the representational gap between the encoder and decoder, SRL achieves state-of-the-art results on video object-centric learning benchmarks. Codes are available at https://github.com/hynnsk/SRL.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
