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The results suggest that effective transfer from 3D reconstruction pre-training to 3D tracking is not merely a matter of partial fine-tuning, but depends on a tracking-oriented transfer principle that preserves shallow geometry while adapting deeper representations to the downstream objective.
SAVTrack estimates the reliability of each candidate vote from both local seed features and inter-frame motion context, and removes low-confidence hypotheses before proposal clustering, preventing unreliable hypotheses from affecting cluster formation while introducing only modest computational overhead.
Misalignment in speculative decoding can lead to a staggering 21-frame error in ASR, but innovative tracking methods can significantly boost efficiency and accuracy.
