Referring video object segmentation relies on natural language descriptions to identify and segment target objects in videos, and has achieved substantial progress in recent years. However, most prior studies process videos in a frame-by-frame manner, failing to fully exploit temporal information. Recently, the large-scale segmentation model Segment Anything Model (SAM) has attracted considerable attention due to its strong segmentation capability and impressive zero-shot generalization. Nevertheless, SAM still exhibits limitations when handling complex action-oriented descriptions. Motivated by these observations, we propose a novel SAM-based Motion-Perception Aggregation framework for referring video object segmentation, termed SAM-MPA, which consists of four modules. DINO-SAM leverages the powerful segmentation ability of SAM to perform initial video segmentation guided by textual prompts, generating object-level masks. The Kalman Filtering Motion Modeling module injects explicit object motion modeling into DINO-SAM, improving segmentation robustness under occlusion and fast-motion scenarios. The motion-aware aggregation module effectively captures object action cues at multiple temporal scales, thereby enhancing global video understanding. The text-token matching module further enforces semantic consistency between the segmentation results and the referring expressions. Extensive experiments on challenging RVOS benchmarks demonstrate that SAM-MPA provides a competitive and efficient SAM-based solution for motion-centric referring video object segmentation, while offering a favorable trade-off between performance and computational cost compared with conventional non-MLLM baselines. The code is available at https://github.com/GXU-LIPE/SAM-MPA.

Quality control in quantitative proteomics is a persistent challenge, particularly in identifying and managing outliers. Unsupervised learning models, which rely on data structure rather than predefined labels, offer potential solutions. However, without clear labels, their effectiveness might be compromised. Single models are susceptible to the randomness of parameters and initialization, which can result in a high rate of false positives. Ensemble models, on the other hand, have shown capabilities in effectively mitigating the impacts of such randomness and assisting in accurately detecting true outliers. Therefore, we introduced SEAOP, a Python toolbox that utilizes an ensemble mechanism by integrating multi-round data management and a statistics-based decision pipeline with multiple models. Specifically, SEAOP uses multi-round resampling to create diverse sub-data spaces and employs outlier detection methods to identify candidate outliers in each space. Candidates are then aggregated as confirmed outliers via a chi-square test, adhering to a 95% confidence level, to ensure the precision of the unsupervised approaches. Additionally, SEAOP introduces a visualization strategy, specifically designed to intuitively and effectively display the distribution of both outlier and non-outlier samples. Optimal hyperparameter models of SEAOP for outlier detection were identified by using a gradient-simulated standard dataset and Mann-Kendall trend test. The performance of the SEAOP toolbox was evaluated using three experimental datasets, confirming its reliability and accuracy in handling quantitative proteomics.