Transformer-based Cross Reference Network for video salient object detection

Abstract

• We propose a Transformer-based network architecture for video salient object detection. • We propose a Gated Cross Reference (GCR) module to facilitate collaborative learning between appearance and motion cues. • The proposed Transformer-based method reaches excellent performance on five public benchmarks. Video salient object detection is a fundamental computer vision task aimed at highlighting the most conspicuous objects in a video sequence. There are two key challenges presented in video salient object detection: (1) how to extract effective feature representations from appearance and motion cues, and (2) how to combine both of them into robust saliency representation. To handle these challenges, in this paper, we propose a novel Transformer-based Cross Reference Network (TCRN), which fully exploits long-range context dependencies in both feature representation extraction and cross-modal (i.e., appearance and motion) integration. In contrast to existing CNN-based methods, our approach formulates video salient object detection as a sequence-to-sequence prediction task. In the proposed approach, the deep feature extraction is achieved by a pure vision transformer with multi-resolution token representations. Specifically, we design a Gated Cross Reference (GCR) module to effectively integrate appearance and motion into saliency representation. The GCR first propagates global context information between different modalities, and then perform cross-modal fusion by a gate mechanism. Extensive evaluations on five widely-used benchmarks show that the proposed Transformer-based method performs favorably against the existing state-of-the-art methods