3.2k citations · 12.3k across the 227 of their papers we have counts for
22 papers · 2 filters
Side-Aware Boundary Localization for More Precise Object Detection
Jiaqi Wang, Wenwei Zhang, Yuhang Cao +6
Current object detection frameworks mainly rely on bounding box regression to localize objects. Despite the remarkable progress in recent years, the precision of bounding box regre…
Learning to Synthesize Fashion Textures
Wu Shi, Tak-Wai Hui, Ziwei Liu +2
Existing unconditional generative models mainly focus on modeling general objects, such as faces and indoor scenes. Fashion textures, another important type of visual elements arou…
Robust Multi-Modality Multi-Object Tracking
Wenwei Zhang, Hui Zhou, Shuyang Sun +3
Multi-sensor perception is crucial to ensure the reliability and accuracy in autonomous driving system, while multi-object tracking (MOT) improves that by tracing sequential moveme…
Delving Deep Into Hybrid Annotations for 3D Human Recovery in the Wild
Yu Rong, Ziwei Liu, Cheng Li +2
Though much progress has been achieved in single-image 3D human recovery, estimating 3D model for in-the-wild images remains a formidable challenge. The reason lies in the fact tha…
One-shot Face Reenactment
Yunxuan Zhang, Siwei Zhang, Yue He +3
To enable realistic shape (e.g. pose and expression) transfer, existing face reenactment methods rely on a set of target faces for learning subject-specific traits. However, in rea…
Learning Lightweight Lane Detection CNNs by Self Attention Distillation
Yuenan Hou, Zheng Ma, Chunxiao Liu +1
Training deep models for lane detection is challenging due to the very subtle and sparse supervisory signals inherent in lane annotations. Without learning from much richer context…
MMDetection: Open MMLab Detection Toolbox and Benchmark
Kai Chen, Jiaqi Wang, Jiangmiao Pang +22
We present MMDetection, an object detection toolbox that contains a rich set of object detection and instance segmentation methods as well as related components and modules. The to…
Disentangling Content and Style via Unsupervised Geometry Distillation
Wayne Wu, Kaidi Cao, Cheng Li +2
It is challenging to disentangle an object into two orthogonal spaces of content and style since each can influence the visual observation differently and unpredictably. It is rare…
Deep Flow-Guided Video Inpainting
Rui Xu, Xiaoxiao Li, Bolei Zhou +1
Video inpainting, which aims at filling in missing regions of a video, remains challenging due to the difficulty of preserving the precise spatial and temporal coherence of video c…
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
Xintao Wang, Kelvin C. K. Chan, Ke Yu +2
Video restoration tasks, including super-resolution, deblurring, etc, are drawing increasing attention in the computer vision community. A challenging benchmark named REDS is relea…
Learning to Cluster Faces on an Affinity Graph
Lei Yang, Xiaohang Zhan, Dapeng Chen +3
Face recognition sees remarkable progress in recent years, and its performance has reached a very high level. Taking it to a next level requires substantially larger data, which wo…
CARAFE: Content-Aware ReAssembly of FEatures
Jiaqi Wang, Kai Chen, Rui Xu +3
Feature upsampling is a key operation in a number of modern convolutional network architectures, e.g. feature pyramids. Its design is critical for dense prediction tasks such as ob…
Self-Supervised Learning via Conditional Motion Propagation
Xiaohang Zhan, Xingang Pan, Ziwei Liu +2
Intelligent agent naturally learns from motion. Various self-supervised algorithms have leveraged motion cues to learn effective visual representations. The hurdle here is that mot…
TransGaGa: Geometry-Aware Unsupervised Image-to-Image Translation
Wayne Wu, Kaidi Cao, Cheng Li +2
Unsupervised image-to-image translation aims at learning a mapping between two visual domains. However, learning a translation across large geometry variations always ends up with…
Path-Restore: Learning Network Path Selection for Image Restoration
Ke Yu, Xintao Wang, Chao Dong +2
Very deep Convolutional Neural Networks (CNNs) have greatly improved the performance on various image restoration tasks. However, this comes at a price of increasing computational…
Prime Sample Attention in Object Detection
Yuhang Cao, Kai Chen, Chen Change Loy +1
It is a common paradigm in object detection frameworks to treat all samples equally and target at maximizing the performance on average. In this work, we revisit this paradigm thro…
Dense Intrinsic Appearance Flow for Human Pose Transfer
Yining Li, Chen Huang, Chen Change Loy
We present a novel approach for the task of human pose transfer, which aims at synthesizing a new image of a person from an input image of that person and a target pose. We address…
Unsupervised Bi-directional Flow-based Video Generation from one Snapshot
Lu Sheng, Junting Pan, Jiaming Guo +3
Imagining multiple consecutive frames given one single snapshot is challenging, since it is difficult to simultaneously predict diverse motions from a single image and faithfully g…
A Lightweight Optical Flow CNN - Revisiting Data Fidelity and Regularization
Tak-Wai Hui, Xiaoou Tang, Chen Change Loy
Over four decades, the majority addresses the problem of optical flow estimation using variational methods. With the advance of machine learning, some recent works have attempted t…
WIDER Face and Pedestrian Challenge 2018: Methods and Results
Chen Change Loy, Dahua Lin, Wanli Ouyang +49
This paper presents a review of the 2018 WIDER Challenge on Face and Pedestrian. The challenge focuses on the problem of precise localization of human faces and bodies, and accurat…
Hybrid Task Cascade for Instance Segmentation
Kai Chen, Jiangmiao Pang, Jiaqi Wang +9
Cascade is a classic yet powerful architecture that has boosted performance on various tasks. However, how to introduce cascade to instance segmentation remains an open question. A…
Region Proposal by Guided Anchoring
Jiaqi Wang, Kai Chen, Shuo Yang +2
Region anchors are the cornerstone of modern object detection techniques. State-of-the-art detectors mostly rely on a dense anchoring scheme, where anchors are sampled uniformly ov…