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Target-Aware Dual Adversarial Learning and a Multi-Scenario Multi-Modality Benchmark To Fuse Infrared and Visible for Object Detection

CVPR 2022 · Detection and segmentation › CNN features · approach: Context for hard detection conditions · 1,179 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use TarDAL fusion instead of DenseFuse fusionYOLOv5 on Multispectral · MultispectralmAP@.5: 0.608 -> 0.613Helped
use TarDAL fusion instead of GANMcC fusionYOLOv5 on M^3FD · M^3FDmAP@.5: 0.797 -> 0.807Helped
add the target discriminator D_T (ablation)TarDAL · M^3FDmAP@.5: 0.719 -> 0.807Helped
add the detail discriminator D_D (ablation)TarDAL · M^3FDmAP@.5: 0.807 -> 0.781Hurt
add both target and detail discriminators (ablation)TarDAL · TNOMI: 2.506 -> 2.766Helped
use cooperative training instead of direct training (ablation)TarDAL on M^3FD · M^3FDmAP@.5: 0.807 -> 0.811Helped
use cooperative training instead of detection-loss-only training (ablation)TarDAL on M^3FD · M^3FDmAP@.5: 0.809 -> 0.811Helped
use cooperative training instead of detection-loss-only training (ablation)TarDAL on Multispectral · MultispectralmAP@.5: 0.615 -> 0.624Helped
remove the saliency degree weight (SDW) pixel loss (ablation)TarDAL · Fusion visual qualitysalient pixel contrastHurt
remove the target mask m from discriminator training (ablation)TarDAL · Fusion visual qualitydetail preservationHurt

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