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Found 10 papers, 5 papers with code
NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results
This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results.
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking.
NTIRE 2024 Challenge on Image Super-Resolution ($\times$4): Methods and Results
This paper reviews the NTIRE 2024 challenge on image super-resolution ($\times$4), highlighting the solutions proposed and the outcomes obtained.
ASUR3D: Arbitrary Scale Upsampling and Refinement of 3D Point Clouds using Local Occupancy Fields
Our proposed implicit occupancy representation enables efficient point classification, effectively discerning points belonging to the surface from non-surface points.
TP-NoDe: Topology-aware Progressive Noising and Denoising of Point Clouds towards Upsampling
TP-NoDe mitigates the need for task-specific training of upsampling networks for a specific upsampling ratio by reusing a point cloud denoising framework.
PointCLIMB: An Exemplar-Free Point Cloud Class Incremental Benchmark
Point clouds offer comprehensive and precise data regarding the contour and configuration of objects.
1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results
The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection.
ABD-Net: Attention Based Decomposition Network for 3D Point Cloud Decomposition
The encapsulated local features are further passed to proposed Attention Feature Encoder to learn basic shapes in point cloud.
Augmented Data as an Auxiliary Plug-in Towards Categorization of Crowdsourced Heritage Data
In this paper, we propose a strategy to mitigate the problem of inefficient clustering performance by introducing data augmentation as an auxiliary plug-in.
Deep Visual Attention-Based Transfer Clustering
In this paper, we propose a methodology to improvise the technique of deep transfer clustering (DTC) when applied to the less variant data distribution.
