  Zero-shot video object segmentation (ZS-VOS) aims to segment foreground
objects in a video sequence without prior knowledge of these objects. However,
existing ZS-VOS methods often struggle to distinguish between foreground and
background or to keep track of the foreground in complex scenarios. The common
practice of introducing motion information, such as optical flow, can lead to
overreliance on optical flow estimation. To address these challenges, we
propose an encoder-decoder-based hierarchical co-attention propagation network
(HCPN) capable of tracking and segmenting objects. Specifically, our model is
built upon multiple collaborative evolutions of the parallel co-attention
module (PCM) and the cross co-attention module (CCM). PCM captures common
foreground regions among adjacent appearance and motion features, while CCM
further exploits and fuses cross-modal motion features returned by PCM. Our
method is progressively trained to achieve hierarchical spatio-temporal feature
propagation across the entire video. Experimental results demonstrate that our
HCPN outperforms all previous methods on public benchmarks, showcasing its
effectiveness for ZS-VOS.

Computer Vision and Pattern Recognition