663 citations · 2.2k across the 65 of their papers we have counts for
14 papers · 1 filter
Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis
Jianwen Xie, Zilong Zheng, Ruiqi Gao +3
3D data that contains rich geometry information of objects and scenes is valuable for understanding 3D physical world. With the recent emergence of large-scale 3D datasets, it beco…
LID 2020: The Learning from Imperfect Data Challenge Results
Yunchao Wei, Shuai Zheng, Ming-Ming Cheng +32
Learning from imperfect data becomes an issue in many industrial applications after the research community has made profound progress in supervised learning from perfectly annotate…
Active Visual Information Gathering for Vision-Language Navigation
Hanqing Wang, Wenguan Wang, Tianmin Shu +2
Vision-language navigation (VLN) is the task of entailing an agent to carry out navigational instructions inside photo-realistic environments. One of the key challenges in VLN is h…
Weakly Supervised 3D Object Detection from Lidar Point Cloud
Qinghao Meng, Wenguan Wang, Tianfei Zhou +3
It is laborious to manually label point cloud data for training high-quality 3D object detectors. This work proposes a weakly supervised approach for 3D object detection, only requ…
Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentation
Guolei Sun, Wenguan Wang, Jifeng Dai +1
This paper studies the problem of learning semantic segmentation from image-level supervision only. Current popular solutions leverage object localization maps from classifiers as…
Video Object Segmentation with Episodic Graph Memory Networks
Xiankai Lu, Wenguan Wang, Martin Danelljan +3
How to make a segmentation model efficiently adapt to a specific video and to online target appearance variations are fundamentally crucial issues in the field of video object segm…
A Unified Object Motion and Affinity Model for Online Multi-Object Tracking
Junbo Yin, Wenguan Wang, Qinghao Meng +2
Current popular online multi-object tracking (MOT) solutions apply single object trackers (SOTs) to capture object motions, while often requiring an extra affinity network to assoc…
Hierarchical Human Parsing with Typed Part-Relation Reasoning
Wenguan Wang, Hailong Zhu, Jifeng Dai +3
Human parsing is for pixel-wise human semantic understanding. As human bodies are underlying hierarchically structured, how to model human structures is the central theme in this t…
Cascaded Human-Object Interaction Recognition
Tianfei Zhou, Wenguan Wang, Siyuan Qi +2
Rapid progress has been witnessed for human-object interaction (HOI) recognition, but most existing models are confined to single-stage reasoning pipelines. Considering the intrins…
Learning Video Object Segmentation from Unlabeled Videos
Xiankai Lu, Wenguan Wang, Jianbing Shen +3
We propose a new method for video object segmentation (VOS) that addresses object pattern learning from unlabeled videos, unlike most existing methods which rely heavily on extensi…
Human-Aware Motion Deblurring
Ziyi Shen, Wenguan Wang, Xiankai Lu +4
This paper proposes a human-aware deblurring model that disentangles the motion blur between foreground (FG) humans and background (BG). The proposed model is based on a triple-bra…
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks
Xiankai Lu, Wenguan Wang, Chao Ma +3
We introduce a novel network, called CO-attention Siamese Network (COSNet), to address the unsupervised video object segmentation task from a holistic view. We emphasize the import…
Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks
Wenguan Wang, Xiankai Lu, Jianbing Shen +2
This work proposes a novel attentive graph neural network (AGNN) for zero-shot video object segmentation (ZVOS). The suggested AGNN recasts this task as a process of iterative info…
Learning Compositional Neural Information Fusion for Human Parsing
Wenguan Wang, Zhijie Zhang, Siyuan Qi +3
This work proposes to combine neural networks with the compositional hierarchy of human bodies for efficient and complete human parsing. We formulate the approach as a neural infor…