Decoupling Multimodal Transformers for Referring Video Object Segmentation - Gao, Mingqi; Yang, Jinyu; Han, Jungong; Lu, Ke; Zheng, Feng; Montana, Giovanni (IEEE transactions on circuits and systems for video technology, 2023)
Referring Video Object Segmentation (RVOS) aims to segment the text-depicted object from video sequences. With excellent capabilities in long-range modelling and information interaction, transformers have been increasingly applied in existing RVOS architectures. To better leverage multimodal data, most efforts focus on the interaction between visual and textual features. However, they ignore the syntactic structures of the text during the interaction, where all textual components are intertwined, resulting in ambiguous vision-language alignment. In this paper, we improve the multimodal interaction by DECOUPLING the interweave. Specifically, we train a lightweight subject perceptron, which extracts the subject part from the input text. Then, the subject and text features are fed into two parallel branches to interact with visual features. This enables us to perform subject-aware and context-aware interactions, respectively, thus encouraging more explicit and discriminative feature embedding and alignment. Moreover, we find the decoupled architecture also facilitates incorporating the vision-language pre-trained alignment into RVOS, further improving the segmentation performance. Experimental results on all RVOS benchmark datasets demonstrate the superiority of our proposed method over the state-of-the-arts. The code of our method is available at: https://github.com/gaomingqi/dmformer.