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Found 4 papers, 2 papers with code
Reveal of Domain Effect: How Visual Restoration Contributes to Object Detection in Aquatic Scenes
According to our analysis, five key discoveries are reported: 1) Domain quality has an ignorable effect on within-domain convolutional representation and detection accuracy; 2) low-quality domain leads to higher generalization ability in cross-domain detection; 3) low-quality domain can hardly be well learned in a domain-mixed learning process; 4) degrading recall efficiency, restoration cannot improve within-domain detection accuracy; 5) visual restoration is beneficial to detection in the wild by reducing the domain shift between training data and real-world scenes.
Rethinking Temporal Object Detection from Robotic Perspectives
From a robotic perspective, the importance of recall continuity and localization stability is equal to that of accuracy, but the AP is insufficient to reflect detectors' performance across time.
Joint Anchor-Feature Refinement for Real-Time Accurate Object Detection in Images and Videos
As for temporal detection in videos, temporal refinement networks (TRNet) and temporal dual refinement networks (TDRNet) are developed by propagating the refinement information across time.
Towards Real-Time Advancement of Underwater Visual Quality with GAN
More specifically, an underwater index is investigated to describe underwater properties, and a loss function based on the underwater index is designed to train the critic branch for underwater noise suppression.
