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Found 7 papers, 4 papers with code
UC-OWOD: Unknown-Classified Open World Object Detection
In this work, we propose a novel OWOD problem called Unknown-Classified Open World Object Detection (UC-OWOD).
HybrUR: A Hybrid Physical-Neural Solution for Unsupervised Underwater Image Restoration
Experimental results show that the proposed method can be used to perform high-quality restoration of unconstrained underwater images without supervision.
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.
Temporally Identity-Aware SSD with Attentional LSTM
Moreover, we develop a creative temporal analysis unit, namely, attentional ConvLSTM (AC-LSTM), in which a temporal attention mechanism is specially tailored for background suppression and scale suppression while a ConvLSTM integrates attention-aware features 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.
