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Found 7 papers, 0 papers with code
ODCR: Orthogonal Decoupling Contrastive Regularization for Unpaired Image Dehazing
ODCR aims to ensure that the haze-related features of the dehazing result closely resemble those of the clear image, while the haze-unrelated features align with the input hazy image.
DFR-Net: Density Feature Refinement Network for Image Dehazing Utilizing Haze Density Difference
In LB, we explore local density features from the dehazing residuals between hazy inputs and proposal images and introduce an Intermediate Dehazing Residual Feedforward (IDRF) module to update local features and pull them closer to clear image features.
FoSp: Focus and Separation Network for Early Smoke Segmentation
We first introduce a Focus module employing bidirectional cascade which guides low-resolution and high-resolution features towards mid-resolution to locate and determine the scope of smoke, reducing the miss detection rate.
Object Preserving Siamese Network for Single Object Tracking on Point Clouds
Exploring an approach that seeks to maximize the preservation of object points and their object-aware features is of particular significance.
Dynamic Background Reconstruction via MAE for Infrared Small Target Detection
Background reconstruction is one of the methods to deal with this problem.
CourtNet for Infrared Small-Target Detection
To deal with this difficulty, this paper proposes a neural-network-based ISTD method called CourtNet, which has three sub-networks: the prosecution network is designed for improving the recall rate; the defendant network is devoted to increasing the precision rate; the jury network weights their results to adaptively balance the precision and recall rate.
DRPN: Making CNN Dynamically Handle Scale Variation
In this paper, we propose a dynamic re-parameterization network (DRPN) to deal with the scale variation and balance the detection precision between small targets and large targets in infrared datasets.
