355 citations · 471 across the 17 of their papers we have counts for
10 papers · 1 filter
High Quality Segmentation for Ultra High-resolution Images
Tiancheng Shen, Yuechen Zhang, Lu Qi +5
To segment 4K or 6K ultra high-resolution images needs extra computation consideration in image segmentation. Common strategies, such as down-sampling, patch cropping, and cascade…
CA-SSL: Class-Agnostic Semi-Supervised Learning for Detection and Segmentation
Lu Qi, Jason Kuen, Zhe Lin +7
To improve instance-level detection/segmentation performance, existing self-supervised and semi-supervised methods extract either task-unrelated or task-specific training signals f…
Image Synthesis via Semantic Composition
Yi Wang, Lu Qi, Ying-Cong Chen +2
In this paper, we present a novel approach to synthesize realistic images based on their semantic layouts. It hypothesizes that for objects with similar appearance, they share simi…
Multi-Scale Aligned Distillation for Low-Resolution Detection
Lu Qi, Jason Kuen, Jiuxiang Gu +5
In instance-level detection tasks (e.g., object detection), reducing input resolution is an easy option to improve runtime efficiency. However, this option traditionally hurts the…
ICM-3D: Instantiated Category Modeling for 3D Instance Segmentation
Ruihang Chu, Yukang Chen, Tao Kong +2
Separating 3D point clouds into individual instances is an important task for 3D vision. It is challenging due to the unknown and varying number of instances in a scene. Existing d…
Fully Convolutional Networks for Panoptic Segmentation with Point-based Supervision
Yanwei Li, Hengshuang Zhao, Xiaojuan Qi +6
In this paper, we present a conceptually simple, strong, and efficient framework for fully- and weakly-supervised panoptic segmentation, called Panoptic FCN. Our approach aims to r…
Open-World Entity Segmentation
Lu Qi, Jason Kuen, Yi Wang +5
We introduce a new image segmentation task, called Entity Segmentation (ES), which aims to segment all visual entities (objects and stuffs) in an image without predicting their sem…
LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-Resolution and Beyond
Wenbo Li, Kun Zhou, Lu Qi +3
Single image super-resolution (SISR) deals with a fundamental problem of upsampling a low-resolution (LR) image to its high-resolution (HR) version. Last few years have witnessed i…
Scale-aware Automatic Augmentation for Object Detection
Yukang Chen, Yanwei Li, Tao Kong +4
We propose Scale-aware AutoAug to learn data augmentation policies for object detection. We define a new scale-aware search space, where both image- and box-level augmentations are…
Best-Buddy GANs for Highly Detailed Image Super-Resolution
Wenbo Li, Kun Zhou, Lu Qi +4
We consider the single image super-resolution (SISR) problem, where a high-resolution (HR) image is generated based on a low-resolution (LR) input. Recently, generative adversarial…