WaveCL: Wavelet Calibration Learning for Referring Video Object Segmentation

Referring video object segmentation (RVOS) focuses on segmenting target objects in a video based on natural language descriptions. However, existing methods typically rely on text cues that are unrelated to video content, and the target entity is only recognized in the pixel space. This often leads to ambiguous cross-modal understanding and fragmented perception across space and time, resulting in inaccurate or incomplete segmentation of the target objects. To address these challenges, a novel wavelet calibration learning (WaveCL) framework is proposed to unify cross-modal understanding and preserve spatial-temporal integrity of the target object. The WaveCL framework is built on two core components: semantic-calibrated entity perception (SEP) and wavelet-guided integrity perception (WIP). SEP aligns the textual semantics with video content, enabling more accurate and context-aware cross-modal understanding. WIP, on the other hand, leverages wavelet representations to capture fine-grained details of the target object from a global spatial-temporal perspective. By refining wavelet clues with the guidance of text queries, WIP enhances the integrity of segmentation. Through the collaboration of SEP and WIP, WaveCL enables precise, target-specific segmentation with detailed boundaries and consistent spatial-temporal perception. Extensive experiments on four benchmark datasets of Ref-YouTube-VOS, Ref-DAVIS17, A2D-Sentences, and JHMDB-Sentences show that WaveCL outperforms existing state-of-the-art methods. The source code of this work can be found in https://mic.tongji.edu.cn.