Computer Vision

Published research on image recognition, object detection, segmentation, and visual understanding from OpenAlex.

Sample of the collection (25 of 180 items)

• DEA-Net: Single Image Dehazing Based on Detail-Enhanced Convolution and Content-Guided Attention Zhejiang University Single image dehazing is a challenging ill-posed problem which estimates latent haze-free images from observed hazy images. Some…
• UAV-YOLOv8: A Small-Object-Detection Model Based on Improved YOLOv8 for UAV Aerial Photography Scenarios Wuhan Institute of Technology Unmanned aerial vehicle (UAV) object detection plays a crucial role in civil, commercial, and military domains. However, the high…
• U-Shape Transformer for Underwater Image Enhancement Beijing Institute of Technology The light absorption and scattering of underwater impurities lead to poor underwater imaging quality. The existing data-driven based…
• Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction National Tsing Hua University We present a super-fast convergence approach to reconstructing the per-scene radiance field from a set of images that capture the scene…
• Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications Hong Kong University of Science and Technology The thriving of artificial intelligence (AI) applications is driving the further evolution of wireless networks. It has been envisioned…
• Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement Dalian University of Technology Low-light image enhancement plays very important roles in low-level vision areas. Recent works have built a great deal of deep learning…
• State of the Art in Defect Detection Based on Machine Vision Tianjin University Abstract Machine vision significantly improves the efficiency, quality, and reliability of defect detection. In visual inspection,…
• Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding City University of Hong Kong Underwater images suffer from color casts and low contrast due to wavelength- and distance-dependent attenuation and scattering. To…
• Sparse Gradient Regularized Deep Retinex Network for Robust Low-Light Image Enhancement City University of Hong Kong Due to the absence of a desirable objective for low-light image enhancement, previous data-driven methods may provide undesirable…
• PointASNL: Robust Point Clouds Processing Using Nonlocal Neural Networks With Adaptive Sampling Chinese University of Hong Kong, Shenzhen Raw point clouds data inevitably contains outliers or noise through acquisition from 3D sensors or reconstruction algorithms. In this…
• PolarMask: Single Shot Instance Segmentation With Polar Representation Group Sense (China) In this paper, we introduce an anchor-box free and single shot instance segmentation method, which is conceptually simple, fully…
• Real-World Underwater Enhancement: Challenges, Benchmarks, and Solutions Under Natural Light Dalian University of Technology Underwater image enhancement is such an important low-level vision task with many applications that numerous algorithms have been…
• HRSID: A High-Resolution SAR Images Dataset for Ship Detection and Instance Segmentation University of Electronic Science and Technology of China With the development of satellite technology, up to date imaging mode of synthetic aperture radar (SAR) satellite can provide higher…
• Augmentation for small object detection Máté Kisantal et al. In the recent years, object detection has experienced impressive progress. Despite these improvements, there is still a significant gap…
• Kindling the Darkness Tianjin University Images captured under low-light conditions often suffer from (partially) poor visibility. Besides unsatisfactory lightings, multiple…
• Lightweight Image Super-Resolution with Information Multi-distillation Network Xidian University In recent years, single image super-resolution (SISR) methods using deep convolution neural network (CNN) have achieved impressive…
• Gated-SCNN: Gated Shape CNNs for Semantic Segmentation University of Waterloo Current state-of-the-art methods for image segmentation form a dense image representation where the color, shape and texture information…
• Local light field fusion University of California System We present a practical and robust deep learning solution for capturing and rendering novel views of complex real world scenes for…
• Deep Stacked Hierarchical Multi-Patch Network for Image Deblurring Data61 Despite deep end-to-end learning methods have shown their superiority in removing non-uniform motion blur, there still exist major…
• Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving Cornell University 3D object detection is an essential task in autonomous driving. Recent techniques excel with highly accurate detection rates, provided…
• DVC: An End-To-End Deep Video Compression Framework Shanghai Jiao Tong University Conventional video compression approaches use the predictive coding architecture and encode the corresponding motion information and…
• LaSOT: A High-Quality Benchmark for Large-Scale Single Object Tracking Temple University In this paper, we present LaSOT, a high-quality benchmark for Large-scale Single Object Tracking. LaSOT consists of 1,400 sequences with…
• BASNet: Boundary-Aware Salient Object Detection University of Alberta Deep Convolutional Neural Networks have been adopted for salient object detection and achieved the state-of-the-art performance. Most of…
• Image Quality Assessment through FSIM, SSIM, MSE and PSNR—A Comparative Study National Institute of Textile Engineering and Research Quality is a very important parameter for all objects and their functionalities. In image-based object recognition, image quality is a…
• The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale Google (Switzerland) We present Open Images V4, a dataset of 9.2M images with unified annotations for image classification, object detection and visual…

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