Bidirectional Anchor-updated Propagation in RefVOS

• BAP is a robust temporal mask propagation technique for Referring Video Object Segmentation that uses high-confidence anchor frames.
• It integrates Moment-Centric Sampling for precise anchor selection and a dynamic memory refresh strategy to combat error drift.
• The bidirectional propagation from key anchors improves segmentation accuracy and temporal consistency in challenging video scenarios.

Bidirectional Anchor-updated Propagation (BAP) is a technique for robust temporal mask propagation in Referring Video Object Segmentation (RefVOS) frameworks. BAP integrates anchor-based initialization, bidirectional mask propagation, and a dynamic memory refresh strategy anchored on model confidence scores. Its design directly addresses the challenges of drift, accumulated error, and unreliable initialization that arise in long video sequences, particularly in tasks demanding high temporal and referential consistency, such as language-guided video segmentation (Dai et al., 10 Oct 2025).

1. Fundamentals of Bidirectional Anchor-updated Propagation

Bidirectional Anchor-updated Propagation comprises three core procedural steps: (1) identification of a high-confidence temporal anchor (the “key moment”), (2) bidirectional mask propagation from this anchor, and (3) dynamic mask refresh based on tracking and prediction confidence metrics. The method is tightly coupled with a Moment-Centric Sampling (MCS) strategy, which selects a compact, high-utility set of frames via Temporal Sentence Grounding (TSG).

The key innovation is to avoid purely sequential (left-to-right) mask propagation, which accumulates errors over time, by introducing a propagation protocol that uses bidirectional flows anchored and periodically refreshed at influential, high-certainty frames.

2. Key Moment Identification and Initialization

Before mask propagation, BAP employs Moment-Centric Sampling (MCS) to identify the most salient temporal anchor frame, denoted $c^*$. Using TSG, the model computes similarity distributions between per-frame tokens and a specialized [FIND] token to determine in which frame the referent object is most optimally grounded.

This “key moment” becomes the starting point for mask initialization. Initializing the segmentation mask at $c^*$—where the referent object is unambiguously visible—mitigates error-prone beginnings, a frequent failure mode especially in long or occlusion-rich videos.

3. Bidirectional Propagation Strategy

Unlike traditional sequential propagation from the first frame, BAP propagates the initialized mask in both temporal directions from $c^*$: forward to subsequent frames and backward to preceding frames. This approach leverages both future and past context, compensating for object appearance/disappearance, occlusions, and motion artifacts.

Bidirectional propagation reduces the risk of error compounding present in strictly unidirectional pipelines. By re-anchoring at intervals, the method also ensures temporal consistency and recovers from local segmentation failures.

4. Dynamic Anchor-updated Memory Mechanism

During the propagation process, segmentation masks are subject to gradual error, especially under challenging motion, occlusions, or appearance changes. BAP introduces a dynamic anchor-updated memory to constrain drift:

• At each sampled key frame (anchor) $i$ (determined via MCS), two scores are computed:
  • The cumulative tracking confidence $\prod_{j=1}^{t} S_j^\mathrm{t}$
  • The current prediction confidence $S_i^\mathrm{p}$
• If the cumulative tracking confidence falls below a threshold proportional to the current prediction confidence (with $\lambda$ the sensitivity hyperparameter), an update flag $U_i$ is set by

$$U_i = \begin{cases} 1 & \text{if}~ \prod_{j=1}^t S_j^\mathrm{t} < \lambda \cdot S_i^\mathrm{p} \ 0 & \text{otherwise} \end{cases}$$

If $U_i = 1$, the current mask memory is cleared and the current anchor’s mask initializes subsequent propagation. This process selectively “cleans” accumulated error and maintains high-fidelity mask propagation throughout the sequence.

5. Integration with Moment-Centric Sampling

BAP is intrinsically linked to Moment-Centric Sampling, which provides the sampling policy for anchor selection. MCS, via TSG and the similarity signal of the [FIND] token, produces a set of keyframes—typically those where the model’s referent grounding is maximized.

Selected frames serve both as efficient temporal summarizations and as periodic anchors for BAP's dynamic refresh strategy. Segmentation is thus “re-anchored” at points of maximum certainty, and computational complexity is mitigated by focusing bidirectional propagation and refresh operations on these high-utility nodes.

Step	Role in BAP	Mechanism
Anchor selection via MCS	Identify high-salience frames	[FIND] token similarity scoring
Bidirectional mask propagation	Mitigate sequential error accumulation	Propagate both forward and backward from anchor
Anchor-updated refresh	Curb drift and maintain tracking stability	Confidence-based mask memory update at anchors

6. Empirical Impact on RefVOS Performance

BAP's initialization from a high-confidence anchor and its bidirectional, dynamically refreshed mask propagation improve both stability and segmentation accuracy, particularly for long or complex videos where temporal reasoning and consistent object tracking are critical.

Reported results demonstrate that BAP substantially enhances metrics such as joint $\mathcal{J}\mathcal{F}$ (global mean of region similarity and contour accuracy) on motion-intensive and reasoning-oriented benchmarks (Dai et al., 10 Oct 2025). This suggests that the architecture’s mechanisms effectively control error drift and support reliable object referent continuity under occlusion, appearance shift, and ambiguous frames.

7. Distinctions from Related Propagation Paradigms

Bidirectional Anchor-updated Propagation shares high-level similarities with other bidirectional or anchor-based propagation architectures (such as certain iterative context-refinement schemes used in document or graph modeling (Roy et al., 3 Oct 2024)), but exhibits critical differences:

• BAP anchors propagation on frames with maximal referent confidence (via MCS), as opposed to propagating across hierarchical levels (e.g., word/sentence/document nodes).
• Anchor updates are governed dynamically by model confidence scores, not fixed graph structure or node centrality.
• BAP is specifically tailored for temporal video segmentation, with the innovation of dual-direction propagation and memory cleaning tightly bound to the demands of long-scope temporal object tracking.
• In contrast, some textual or graph models implement bidirectional information flow for hierarchical refinement, but without anchor-driven, confidence-based memory refresh.

These design choices make BAP especially robust for long-term video referent tracking, providing a strategy to reinitialize from trustworthy contexts and counteract accumulation of segmentation error due to challenging temporal dynamics.

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Bidirectional Anchor-updated Propagation thus represents a targeted solution for maintaining segmentation accuracy and tracking integrity in video-based visual-linguistic tasks. Its integration of confidence-driven anchor selection, bidirectional mask propagation, and dynamic memory refresh mechanisms directly addresses the susceptibility to accumulated error present in traditional mask propagation approaches (Dai et al., 10 Oct 2025).