This paper focuses on developing a more effective method of hierarchical propagation for semi-supervised Video Object Segmentation (VOS). Based on vision transformers, the recently-developed Associating Objects with Transformers (AOT) approach introduces hierarchical propagation into VOS and has shown promising results. The hierarchical propagation can gradually propagate information from past frames to the current frame and transfer the current frame feature from object-agnostic to object-specific. However, the increase of object-specific information will inevitably lead to the loss of object-agnostic visual information in deep propagation layers. To solve such a problem and further facilitate the learning of visual embeddings, this paper proposes a Decoupling Features in Hierarchical Propagation (DeAOT) approach.

The optimization strategies and related hyper-parameters are also the same as AOT. The loss function is a 0.5:0.5 combination of BCE loss [ Such a process is necessary to keep enough long-term information and avoid facing out of memory when inferring long videos. The longest video in VOT 2020 contains 1,500 frames. We compare our DeAOT with more VOS methods in Table 2 and 1. VOS cases, including similar objects, occlusion, fast motion, motion blur, etc. A.4 Border Impact and Limitations The proposed DeAOT framework significantly improves VOS's performance, robustness, and robustness. As to limitations, the scenarios with multiple similar objects and severe occlusions are still very challenging for DeAOT and other VOS solutions.

This paper focuses on developing a more effective method of hierarchical propagation for semi-supervised Video Object Segmentation (VOS). Based on vision transformers, the recently-developed Associating Objects with Transformers (AOT) approach introduces hierarchical propagation into VOS and has shown promising results. The hierarchical propagation can gradually propagate information from past frames to the current frame and transfer the current frame feature from object-agnostic to object-specific. However, the increase of object-specific information will inevitably lead to the loss of object-agnostic visual information in deep propagation layers. To solve such a problem and further facilitate the learning of visual embeddings, this paper proposes a Decoupling Features in Hierarchical Propagation (DeAOT) approach. Secondly, to compensate for the additional computation from dual-branch propagation, we propose an efficient module for constructing hierarchical propagation, i.e., Gated Propagation Module, which is carefully designed with single-head attention.