Jonas Seng

The work presents SnapLog, a pipeline that encodes each video frame with a pre-trained ViT, performs temporal segmentation through a frame-wise similarity matrix with K-Means and a greedy merge, then applies generalized few-shot classification with a 34-layer R(2+1)D encoder pre-trained on Sports-1M plus a linear head to convert raw video into timestamped event logs (either deterministic or uncertainty-aware logs that retain label probability distributions); on 16 brownie-baking videos from TUM Kitchen and 24 kitchen-cleaning videos from Epic Kitchens-100 it achieves frame-wise segmentation accuracy of 74.3% and 67.7%, and with augmentation plus 100 overlapping clips Top-3 classification accuracy of 90.1% and 85.

The work presents SnapLog, a pipeline that encodes each video frame with a pre-trained ViT, performs temporal segmentation through a frame-wise similarity matrix with K-Means and a greedy merge, then applies generalized few-shot classification with a 34-layer R(2+1)D encoder pre-trained on Sports-1M plus a linear head to convert raw video into timestamped event logs (either deterministic or uncertainty-aware logs that retain label probability distributions); on 16 brownie-baking videos from TUM Kitchen and 24 kitchen-cleaning videos from Epic Kitchens-100 it achieves frame-wise segmentation accuracy of 74.3% and 67.7%, and with augmentation plus 100 overlapping clips Top-3 classification accuracy of 90.1% and 85.