Referring Video Object Segmentation (RVOS) aims to segment objects in videos based on language expressions. In this task, existing methods often face challenges due to semantic misalignment between language and visual modalities. To address this, this paper categorizes semantic misalignment into word-level, sentence-level, and cognitive-level issues. For these issues, we propose the Dynamic Alignment of Visual and Language Semantics (DAVLS) framework. In DAVLS, to address word-level semantic misalignment, we design the Word-level Semantic Dynamic Alignment (WSDA) to maximize the alignment of word-level semantics with appropriate visual features by embedding them into frame-level visual features and enabling cross-modal fusion visually. Based on accurate sentence-level object semantics generated by the textual adapter (TA), we propose the Sentence-level Semantic Dynamic Alignment (SSDA) to address sentence-level misalignment by aligning these semantics with the visual object state of the video clip. Due to the intrinsic linkage between WSDA and SSDA, they are integrated into a unified module, named SDA. Moreover, to mitigate the cognitive-level misalignment between humans and the segmentation model in understanding the semantics of referring objects, we introduce Semantic Augmentation (SA) to enhance the model’s comprehension of object semantics by expanding linguistic expressions. Simulation results on Ref-YouTube-VOS (3,978 videos), Ref-DAVIS17 (90 videos), A2D-Sentences (3,782 videos), and JHMDB-Sentences (928 videos) demonstrate the competitive performance of DAVLS, achieving a 0.9% improvement in J & F on Ref-YouTube-VOS and a 1.0% increase in Overall IoU on A2D-Sentences. Moreover, a statistically significant result ( p = . 03125 ), validated by the Wilcoxon signed-rank test, confirms the reliability of the performance gains.

This work tackles the seldom discussed task of predicting chaotic time series generated by dynamic systems with evolving parameters. Representative chaotic time series produced by different system dimensions are introduced with a critical parameter linearly depending on time. The evolving character of systems are qualitatively studied by phase portraits. We assess the predictability of different fuzzy neural network (FNN) architectures on several evolving chaotic time series. Experiments illustrate that FNN models can generally better approximate evolving chaotic systems comparing to the autoregression method as a benchmark. The main contribution of our work is that we found out certain FNN types, e.g., NEFCON and DENFIS, are more robust to changing system parameters. In spite of the performance, some FNN models are more vulnerable and incline to be destabilized by high order chaotic systems. This work also casts light on composing FNN structures to capture evolving characters of chaotic time series in the future.