355 citations · 2.9k across the 115 of their papers we have counts for
25 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…
On Efficient Transformer-Based Image Pre-training for Low-Level Vision
Wenbo Li, Xin Lu, Shengju Qian +3
Pre-training has marked numerous state of the arts in high-level computer vision, while few attempts have ever been made to investigate how pre-training acts in image processing sy…
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…
Blending Anti-Aliasing into Vision Transformer
Shengju Qian, Hao Shao, Yi Zhu +2
The transformer architectures, based on self-attention mechanism and convolution-free design, recently found superior performance and booming applications in computer vision. Howev…
Guided Point Contrastive Learning for Semi-supervised Point Cloud Semantic Segmentation
Li Jiang, Shaoshuai Shi, Zhuotao Tian +4
Rapid progress in 3D semantic segmentation is inseparable from the advances of deep network models, which highly rely on large-scale annotated data for training. To address the hig…
Deep Structured Instance Graph for Distilling Object Detectors
Yixin Chen, Pengguang Chen, Shu Liu +2
Effectively structuring deep knowledge plays a pivotal role in transfer from teacher to student, especially in semantic vision tasks. In this paper, we present a simple knowledge s…
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…
PFENet++: Boosting Few-shot Semantic Segmentation with the Noise-filtered Context-aware Prior Mask
Xiaoliu Luo, Zhuotao Tian, Taiping Zhang +3
In this work, we revisit the prior mask guidance proposed in ``Prior Guided Feature Enrichment Network for Few-Shot Segmentation''. The prior mask serves as an indicator that highl…
Exploring and Improving Mobile Level Vision Transformers
Pengguang Chen, Yixin Chen, Shu Liu +2
We study the vision transformer structure in the mobile level in this paper, and find a dramatic performance drop. We analyze the reason behind this phenomenon, and propose a novel…
Parametric Contrastive Learning
Jiequan Cui, Zhisheng Zhong, Shu Liu +2
In this paper, we propose Parametric Contrastive Learning (PaCo) to tackle long-tailed recognition. Based on theoretical analysis, we observe supervised contrastive loss tends to b…
Conditional Temporal Variational AutoEncoder for Action Video Prediction
Xiaogang Xu, Yi Wang, Liwei Wang +2
To synthesize a realistic action sequence based on a single human image, it is crucial to model both motion patterns and diversity in the action video. This paper proposes an Actio…
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…
Semi-supervised Semantic Segmentation with Directional Context-aware Consistency
Xin Lai, Zhuotao Tian, Li Jiang +4
Semantic segmentation has made tremendous progress in recent years. However, satisfying performance highly depends on a large number of pixel-level annotations. Therefore, in this…
MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution
Liying Lu, Wenbo Li, Xin Tao +2
Reference-based image super-resolution (RefSR) has shown promising success in recovering high-frequency details by utilizing an external reference image (Ref). In this task, textur…
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…
Distilling Knowledge via Knowledge Review
Pengguang Chen, Shu Liu, Hengshuang Zhao +1
Knowledge distillation transfers knowledge from the teacher network to the student one, with the goal of greatly improving the performance of the student network. Previous methods…
Improving Calibration for Long-Tailed Recognition
Zhisheng Zhong, Jiequan Cui, Shu Liu +1
Deep neural networks may perform poorly when training datasets are heavily class-imbalanced. Recently, two-stage methods decouple representation learning and classifier learning to…
Jigsaw Clustering for Unsupervised Visual Representation Learning
Pengguang Chen, Shu Liu, Jiaya Jia
Unsupervised representation learning with contrastive learning achieved great success. This line of methods duplicate each training batch to construct contrastive pairs, making eac…
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…
Bidirectional Projection Network for Cross Dimension Scene Understanding
Wenbo Hu, Hengshuang Zhao, Li Jiang +2
2D image representations are in regular grids and can be processed efficiently, whereas 3D point clouds are unordered and scattered in 3D space. The information inside these two vi…
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…
Video Instance Segmentation with a Propose-Reduce Paradigm
Huaijia Lin, Ruizheng Wu, Shu Liu +2
Video instance segmentation (VIS) aims to segment and associate all instances of predefined classes for each frame in videos. Prior methods usually obtain segmentation for a frame…