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UCF-STAR: A Large Scale Still Image Dataset for Understanding Human Actions

AAAI 2020 · Video › Action recognition · approach: Disentangled action recognition · 10 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add the predicted dynamic-skeleton temporal stream to the spatial stream (ablation)TSSTN · UCF-STAR testmAP (%): 26.3 -> 91.9Helped
add the spatial stream to the temporal stream (ablation)TSSTN · UCF-STAR testmAP (%): 86.2 -> 91.9Helped
fine-tune only top 7 layers instead of all layers in the temporal stream (ablation)temporal stream · UCF-STAR testmAP (%): 68.3 -> 86.2Helped
fine-tune a pretrained network instead of training the temporal stream from scratch (ablation)temporal stream · UCF-STAR testmAP (%): 61.3 -> 86.2Helped
use custom top-k loss instead of default softmax loss for ds prediction (ablation)dynamic-skeleton predictor · predicted ds mAP, 500 epochsmAP: 76% -> 83%Helped
fine-tune VGG instead of training ds predictor from scratch (ablation)dynamic-skeleton predictor · predicted ds mAP, 500 epochsmAP: 65% -> 76%Helped
TSSTN vs prior still-image methodsTSSTN · UCF-STAR testmAP (%): 70.9 -> 91.9Helped
TSSTN vs best prior on Stanford-40 (all classes)TSSTN · Stanford-40 (All)mAP (%): 90.9 -> 86.3Hurt
TSSTN on body-motion vs non-body-motion Stanford-40 classes compared with Gkioxari et al.TSSTN · Stanford-40mAP (%): 93.87 -> 97.8 (body-motion); 89.73 -> 80.2 (non-body-motion)Mixed
TSSTN vs best prior on Willow body-motionTSSTN · Willow overallmAP (%): 79.3 -> 84.6Helped
TSSTN vs best prior on WIDERTSSTN · WIDER body-motionmAP (%): 86.2 -> 90.3Helped

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