RS-RVOS Bench: Causality-Aware Segmentation

Updated 24 January 2026

RS-RVOS Bench is a large-scale dataset with strict online causality and advanced annotations for remote sensing video object segmentation.
It employs a causality-aware protocol that restricts queries to the initial frame, addressing challenges like weak saliency, occlusion, and visual ambiguity.
Benchmarking shows that methods like MQC-SAM achieve significant performance gains, underscoring the dataset’s robustness and real-time evaluation capabilities.

RS-RVOS Bench is a large-scale, causality-aware dataset specifically constructed for the evaluation and advancement of remote sensing referring video object segmentation (RS-RVOS). Distinct from prior RVOS resources, RS-RVOS Bench introduces strict online causality in annotation and is tailored to the unique challenges of dynamic remote sensing scenarios, such as weak target saliency, occlusion, and visual ambiguity.

1. Dataset Construction and Protocol

RS-RVOS Bench contains 111 remote sensing video sequences, comprising approximately 25,000 frames and 213,000 temporal referring annotations. The dataset is partitioned into 82 training sequences (18,500 frames, 159,000 annotations) and 29 testing sequences (6,500 frames, 54,000 annotations). Videos span resolutions from 502×512 to 2160×1080 pixels across 11 semantic categories such as ‘aircraft’ and ‘ship’.

Annotation follows a causality-aware protocol: for every sequence, natural-language referring expressions are generated solely from the initial frame ( $I_0$ ). Spatial references are composed by dividing $I_0$ into a $3 \times 3$ grid and mapping target centroids to locational descriptors. Attribute references derive from an enumerated set of 11 fine-grained classes. No future frames are permitted for context, removing temporal leakage and enforcing strict online segmentation conditions.

Dataset curation uses a Visual Discriminability Score (VDS) to filter sequences based on inter-object contrast, density, and blur. The benchmark encompasses real-world challenges such as weak target saliency, dense distractors, and pausing occlusions. This design differentiates RS-RVOS Bench from natural scene RVOS datasets, which typically allow annotators to exploit full-video context, produce expressions post hoc, and underrepresent remote sensing attributes (Jiang et al., 17 Jan 2026).

	Sequences	Frames	Annotations
RS-RVOS Bench (total)	111	25,000	213,000
Training split	82	18,500	159,000
Testing split	29	6,500	54,000
Semantic categories	\multicolumn{3}{c}{11}
Resolution range	\multicolumn{3}{c}{502×512–2160×1080}

2. Benchmarking Task and Evaluation Metrics

The central task is online referring video object segmentation under causality constraints: only the initial frame $I_0$ and preceding frames are available at time $t$ , with no access to future data. Each instance is defined by a single textual query $Q$ describing the target as observed at $I_0$ .

Predictions are temporally evaluated using three principal metrics:

Region similarity (Jaccard index): $\mathcal{J} = \frac{|R_t \cap G_t|}{|R_t \cup G_t|}$ , where $R_t$ is the predicted mask and $G_t$ is the ground truth at frame $t$ .
Contour accuracy (F-score): $\mathcal{F} = \frac{2P_cR_c}{P_c+R_c}$ , where $P_c$ and $R_c$ denote precision and recall over mask contours.
Combined score: $\frac{1}{2}(\mathcal{J}+\mathcal{F})$ .

[email protected] is reported for both $\mathcal{J}$ and $\mathcal{F}$ , highlighting robustness at high-quality thresholds (Jiang et al., 17 Jan 2026).

3. Baseline Methods and Empirical Performance

Multiple backbone models were benchmarked on RS-RVOS Bench, including referring transformer-based, online memory, and open-world generalist segmentation methods:

Method	J{data}F	J-Mean	J-Rec	F-Mean	F-Rec
ReferFormer	0.526	0.296	0.196	0.756	0.811
OnlineRefer	0.324	0.149	0.083	0.500	0.551
VISA	0.215	0.118	0.014	0.312	0.241
SAMWISE	0.481	0.317	0.306	0.645	0.682
SOC	0.539	0.348	0.267	0.730	0.761
ReferDINO (B-Swin)	0.562	0.303	0.210	0.821	0.880
MQC-SAM	0.712	0.506	0.541	0.918	0.952

A significant empirical gap is observed between MQC-SAM (the proposed method) and the best prior baseline (ReferDINO, J{data}F = 0.562), demonstrating the considerable task complexity induced by the causality-aware protocol and remote sensing conditions.

4. MQC-SAM: Benchmark-Leading Methodology

MQC-SAM (Memory-Quality Control with Segment Anything Model) is a two-stage online segmentation architecture tailored for RS-RVOS Bench:

Stage 1: Temporal Motion Consistency Calibration (TMCC)

An initial mask $R_0$ is generated by the fusion of visual and text encoders.
Short-term dense displacements $\mathbf{d}_t$ are accumulated over an adaptive window $n^*$ , computing per-pixel average motion $D(x,y)$ .
Statistical constraints (mean $\mu_d$ , std $\sigma_d$ ) expand and prune $R_0$ to enforce motion-field alignment, forming a refined and anchored mask.

Stage 2: Decoupled Attention Memory Integration (DAMI)

The memory bank $\mathcal{M}_t$ is decomposed into three orthogonal stores: cross-modal semantic anchor $\mathbf{A}_{sem}$ (fixed), a short-term FIFO buffer $\mathcal{F}_{mem}$ , and a discriminative prototype pool $\mathcal{F}_{disc}$ .
Attention-weighted fusion integrates these for segmentation.
Dynamic quality assessment (via confidence $\alpha_t$ ) modulates memory updates, filtering unreliable or low-confidence feature increments and reducing error propagation.

Ablation studies confirm both TMCC and DAMI are essential: TMCC alone yields a +4.4 pp gain in J{data}F, DAMI alone gives +2.3 pp, and the combined system achieves the highest benchmark score (Jiang et al., 17 Jan 2026).

RS-RVOS Bench is the only large-scale, causality-enforced evaluation set in the RS-RVOS literature. Its design contrasts with MeViS (used in the LSVOS RVOS tracks (Niu et al., 21 Sep 2025, Tran, 2024)), Long-RVOS (Liang et al., 19 May 2025), and other generic RVOS datasets in three principal ways:

Annotation causality: Only initial frame visibility for linguistic query construction, in contrast to retrospective or full-context annotation in MeViS or Long-RVOS.
Domain specificity: All content is remote sensing, addressing low resolution, weak saliency, and occlusion typical of satellite/aerial video but uncommon in natural scene datasets.
Evaluation focus: Prioritizes real-time, online models and penalties for error accumulation—addressing RS-specific operational requirements absent from generic RVOS tracks.

The adoption of RS-RVOS Bench is therefore positioned to foster specialized algorithmic innovation in remote sensing object segmentation, emphasizing practical deployment capabilities under information-truncated causal constraints (Jiang et al., 17 Jan 2026).

6. Forward Directions and Anticipated Impact

By introducing a benchmark where causality, weak signals, and challenging distractors are ubiquitous, RS-RVOS Bench highlights the need for advanced memory control, robust motion integration, and semantically grounded attentional mechanisms. Its strict protocol precludes techniques that rely on future leakage or off-line aggregation. The superior performance of MQC-SAM demonstrates the efficacy of memory-quality-aware update schemes and the importance of motion–semantic fusion under challenging remote sensing settings.

It is expected that RS-RVOS Bench will serve as the standard for future RS-RVOS research, supporting the development of architectures that generalize to real-world, dynamic, and causally constrained remote sensing datasets used in Earth observation and defense scenarios (Jiang et al., 17 Jan 2026).