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Egocentric Video Task Translation

CVPR 2023 · Video › Video-language understanding · approach: Egocentric video understanding · 13 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
translate auxiliary-task features with EgoT2-stask-specific models · OSCCaccuracy (%): 68.22 -> 72.69Helped
translate auxiliary-task features with EgoT2-stask-specific models · TTMmAP (%): 58.91 -> 66.54Helped
translate auxiliary-task features with EgoT2-stask-specific models · ASDmAP (%): 79.05 -> 79.38Helped
use one task-general translator for all tasksEgoT2-g · TTMmAP (%): 58.91 -> 64.49Helped
replace the TTM auxiliary-task models with TTM-only models (ablation)EgoT2-s · TTMmAP (%): 63.40 -> 66.54Helped
preserve temporal information instead of pooling task features (ablation)EgoT2-s · TTMmAP (%): 65.47 -> 66.54Helped
freeze task-specific models during second-stage training (ablation)EgoT2-s · TTMmAP (%): 66.00 -> 66.54No clear effect
add auxiliary-task models rather than use OSCC-only models (ablation)EgoT2-s · OSCCmAP (%): 69.68 -> 72.69Helped
preserve temporal information instead of pooling task features (ablation)EgoT2-s · OSCCmAP (%): 71.65 -> 72.69Helped
freeze task-specific models during second-stage training (ablation)EgoT2-s · OSCCmAP (%): 72.22 -> 72.69No clear effect
replace the SlowFast backbone with EgoVLP while retaining task translationEgoT2-s · OSCCaccuracy (%): 73.00 -> 75.77Helped
include LAM from the other task clusterEgoT2-g · PNRtemporal localization error (s): 0.612 -> 0.611No clear effect

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