arXiv (All)
open access
TSA: Temporal Slot Activation for Persistent Object-Centric Video Representation
Unsupervised video object-centric learning aims to decompose dynamic scenes into temporally persistent entity representations. Existing recurrent video slot-attention methods propagate a fixed set of slots across frames, but typically assume unconditional slot propagation: every slot is updated and
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Dual-State Slot Attention: Decoupling Appearance and Identity for Video Object-Centric Learning#1387092
- Temporal State Transport in Video Generation: Diagnosing and Correcting Spectral Imbalance#985854
- WorldAttention: An Efficient Attention Architecture for Interactive Video World Models#1128868
- Rethinking Object-Centric Representations for Video Dynamics Modeling#1128614
- Rethinking Temporal Consistency in Video Object-Centric Learning: From Prediction to Correspondence#155316
Record · ID 1386303
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive