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Low-Fidelity Video Encoder Optimization for Temporal Action Localization

NeurIPS 2021 · Video › Action recognition · approach: Weakly supervised temporal action localization · 16 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add LoFi end-to-end video encoder optimization with TAL loss (Long C-LoFi) after classification pre-training (ablation)G-TAD with TSM-R18 · ActivityNet-v1.3average mAP: 33.59 -> 34.49Helped
add LoFi end-to-end video encoder optimization (ablation)G-TAD with TSM-R18 · HACS-v1.1average mAP: 23.00 -> 24.64Helped
fine-tune encoder with action classification on target positive segments (ACP+) instead of LoFi (ablation)G-TAD with TSM-R18 · ActivityNet-v1.3average mAP: 33.59 -> 33.84No clear effect
replace Kinetics action pre-training with ImageNet image pre-training (ablation)G-TAD with TSM-R18 · HACS-v1.1average mAP: 23.00 -> 20.18Hurt
use spatial-only low fidelity (S-LoFi) rather than standard training (ablation)G-TAD with TSM-R18 · ActivityNet-v1.3average mAP: 33.59 -> 34.12Helped
use temporal-only low fidelity (T-LoFi) (ablation)G-TAD with TSM-R18 · ActivityNet-v1.3average mAP: 33.59 -> 34.32Helped
cycle through low-fidelity configurations (Long C-LoFi) rather than single configuration (ablation)LoFi · ActivityNet-v1.3average mAP: 34.32 -> 34.49Helped
add LoFi to R(2+1)D-R18 encoder (ablation)G-TAD · ActivityNet-v1.3average mAP: 33.55 -> 34.21Helped
add LoFi to deeper TSM-R50 encoder (ablation)G-TAD · ActivityNet-v1.3average mAP: 34.26 -> 34.96Helped
add self-supervised pre-training (Arrow of Time) on top of ACPG-TAD with TSM-R18 · ActivityNet-v1.3average mAP: 33.59 -> 33.72No clear effect
add SpeedNet self-supervision on top of ACPG-TAD with TSM-R18 · ActivityNet-v1.3average mAP: 33.59 -> 33.75No clear effect

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