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About this paper
Taekyung Kim, Jeongeun Park, Byeongho Heo, Sungjoon Choi, Sangdoo Yun, Dongyoon Han
SPARK adapts pretrained vision models for robot control by training a compact frame summary to predict later visual features while aligning adapted patch features with those of the frozen model. For CLIP, its cross-benchmark aggregate rises from 50.7 to 56.0, a mixed-metric average; gains depend on backbone capacity, and its temporal objective predicts one offset scene, not a future sequence.
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