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 decoded at every frame, regardless of whether its corresponding object is visible. We show that this design violates a basic lifecycle requirement for persistent slots: when an object is absent or fully occluded, its slot should preserve its previous state and avoid explaining unrelated visible content. Instead, unconditional propagation creates *update-induced state drift*, where current-frame evidence overwrites the absent object's representation, and *decoder-induced reconstruction interference*, where the inactive slot remains coupled to reconstruction through decoder attention. We propose **Temporal Slot Activation (TSA)**, a lightweight mechanism that learns a per-slot, per-frame activation score ${\alpha }_{k,t}\in (0,1)$ without visibility supervision. TSA uses this activation as a shared latent control variable for slot lifecycle modeling. When a slot is inactive, TSA anchors its state to the previous slot through activation-gated updating and suppresses its decoder participation through an activation-dependent additive bias on attention logits before softmax normalization. This jointly reduces state drift and reconstruction-driven interference. To improve decisions under partial occlusion and gradual reappearance, TSA further conditions activation prediction on a per-slot temporal memory produced by a Temporal Context Encoder. We evaluate TSA on MOVi-C, MOVi-E, YouTube-VIS HQ, and the occlusion-heavy OVIS benchmarks, using both standard metrics (FG-ARI, mBO) and tracking-based metrics (IDF1, HOTA). TSA improves object decomposition and temporal identity preservation, with large gains on long, heavily occluded videos. Source code will be released upon publication.