This paper introduces DSS-V, a novel framework extending Dichotomous Image Segmentation to video sequences, addressing challenges in segmenting camouflaged objects in videos. It includes a Temporal Consistency Module, Multi-Scale Spatio-Temporal Fusion, and frequency-domain priors for boundary detection.
Key findings
DSS-V extends DIS principles to video domain for camouflaged object segmentation.
Introduces Temporal Consistency Module for implicit motion learning and spatio-temporal coherence.
Proposes Multi-Scale Spatio-Temporal Fusion for precise boundary detection.
Includes frequency-domain priors for challenging camouflage scenarios.
Limitations & open questions
The computational cost of high-resolution DIS processing for video sequences.
The reliability of explicit motion estimation for minimal motion camouflaged objects.