CroBIM-V: Memory-Quality Controlled Remote Sensing Referring Video Object Segmentation

TL;DR

This paper introduces RS-RVOS Bench, a large-scale dataset for remote sensing video referring object segmentation, and proposes MQC-SAM, a memory-quality-aware segmentation framework that improves accuracy by filtering unreliable information.

Contribution

The paper provides the first large-scale RS-RVOS benchmark and a novel memory management framework that enhances segmentation accuracy and robustness in dynamic remote sensing videos.

Findings

MQC-SAM achieves state-of-the-art performance on RS-RVOS Bench.

The dataset adopts causality-aware annotations for more realistic scenarios.

Memory quality control effectively prevents error propagation.

Abstract

Remote sensing video referring object segmentation (RS-RVOS) is challenged by weak target saliency and severe visual information truncation in dynamic scenes, making it extremely difficult to maintain discriminative target representations during segmentation. Moreover, progress in this field is hindered by the absence of large-scale dedicated benchmarks, while existing models are often affected by biased initial memory construction that impairs accurate instance localization in complex scenarios, as well as indiscriminate memory accumulation that encodes noise from occlusions or misclassifications, leading to persistent error propagation. This paper advances RS-RVOS research through dual contributions in data and methodology. First, we construct RS-RVOS Bench, the first large-scale benchmark comprising 111 video sequences, about 25,000 frames, and 213,000 temporal referring annotations. Unlike common RVOS benchmarks where many expressions are written with access to the full video context, our dataset adopts a strict causality-aware annotation strategy in which linguistic references are generated solely from the target state in the initial frame. Second, we propose a memory-quality-aware online referring segmentation framework, termed Memory Quality Control with Segment Anything Model (MQC-SAM). MQC-SAM introduces a temporal motion consistency module for initial memory calibration, leveraging short-term motion trajectory priors to correct structural deviations and establish accurate memory anchoring. Furthermore, it incorporates a decoupled attention-based memory integration mechanism with dynamic quality assessment, selectively updating high-confidence semantic features while filtering unreliable information, thereby effectively preventing error accumulation and propagation. Extensive experiments on RS-RVOS Bench demonstrate that MQC-SAM achieves state-of-the-art performance.