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Abstract To address the dual challenges of scarce defect samples in power line component images and fine-grained defects being easily overwhelmed in complex backgrounds, this paper proposes a video world model prior-driven component defect recognition framework, TransVJ2-MCA. First, a cross-modal temporal reconstruction (CMTR) mechanism is designed to adapt to the input constraints of the video self-supervised pre-trained backbone network (V-JEPA v2).
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