34 papers
HSA: Hierarchical Slot Attention for Multi-granularity Scene-Decomposition
Neelu Madan, Rongzhen Zhao, Andreas Mogelmose +4
Slot attention is a powerful framework for object-centric learning, decomposing visual scenes into latent slots through iterative competitive attention. However, existing methods s…
GeoMix: Descriptor-Free Visual Localization via Global Context and Multi-Detector Training
Yejun Zhang, Xinjue Wang, Zihan Wang +2
Descriptor-free visual localization eliminates high-dimensional descriptor storage, preserves scene privacy, and simplifies map maintenance, yet its accuracy still lags far behind…
Efficient Reinforcement Learning by Guiding World Models with Non-Curated Data
Yi Zhao, Aidan Scannell, Wenshuai Zhao +7
Leveraging offline data is a promising way to improve the sample efficiency of online reinforcement learning (RL). This paper expands the pool of usable data for offline-to-online…
Internalizing Temporal Consistency in Video Object-Centric Learning without Explicit Regularization
Rongzhen Zhao, Zhiyuan Li, Juho Kannala +1
Video Object-Centric Learning (OCL) aims to represent objects as \textit{slot} vectors and maintain their consistency across frames. Slot-Slot Contrastive (SSC) loss has become the…
Cycle Consistency in Video Object-Centric Learning
Rongzhen Zhao, Zhiyuan Li, Ruonan Wei +2
Self-supervised video Object-Centric Learning (OCL) aims to discover distinct objects and associate them across time, whereas self-supervised Multi-Object Tracking (MOT) focuses on…
Smoothing Slot Attention Iterations and Recurrences
Rongzhen Zhao, Wenyan Yang, Juho Kannala +1
Slot Attention (SA) lies at the heart of mainstream Object-Centric Learning (OCL). Image features can be aggregated into object-level representations by SA \textit{iteratively} ref…