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Found 10 papers, 3 papers with code
Always Clear Days: Degradation Type and Severity Aware All-In-One Adverse Weather Removal
The proposed method can outperform the state-of-the-art methods subjectively and objectively on different weather removal tasks with a large margin, and enjoy less model parameters.
Perspective-aware Convolution for Monocular 3D Object Detection
Monocular 3D object detection is a crucial and challenging task for autonomous driving vehicle, while it uses only a single camera image to infer 3D objects in the scene.
Panoptic-Depth Color Map for Combination of Depth and Image Segmentation
Image segmentation and depth estimation are crucial tasks in computer vision, especially in autonomous driving scenarios.
Domain Adaptation for Underwater Image Enhancement via Content and Style Separation
Underwater image suffer from color cast, low contrast and hazy effect due to light absorption, refraction and scattering, which degraded the high-level application, e. g, object detection and object tracking.
Explorable Tone Mapping Operators
It aims to preserve visual information of HDR images in a medium with a limited dynamic range.
Difference-Seeking Generative Adversarial Network--Unseen Sample Generation
Unseen data, which are not samples from the distribution of training data and are difficult to collect, have exhibited importance in numerous applications, ({\em e. g.,} novelty detection, semi-supervised learning, and adversarial training).
Difference-Seeking Generative Adversarial Network
DSGAN considers the scenario that the training samples of target distribution, $p_{t}$, are difficult to collect.
Discrete Gyrator Transforms: Computational Algorithms and Applications
Besides, we propose a kind of DGT based on the eigenfunctions of the gyrator transform.
Two-dimensional nonseparable discrete linear canonical transform based on CM-CC-CM-CC decomposition
Simulation results show that the proposed methods have higher accuracy, lower computational complexity and smaller error in the additivity property compared with the previous works.
Fast Binary Embedding via Circulant Downsampled Matrix -- A Data-Independent Approach
Binary embedding of high-dimensional data aims to produce low-dimensional binary codes while preserving discriminative power.
