663 citations · 3.7k across the 62 of their papers we have counts for
22 papers · 2 filters
Learning to Fuse Asymmetric Feature Maps in Siamese Trackers
Wencheng Han, Xingping Dong, Fahad Shahbaz Khan +2
Recently, Siamese-based trackers have achieved promising performance in visual tracking. Most recent Siamese-based trackers typically employ a depth-wise cross-correlation (DW-XCor…
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…
Siamese Network for RGB-D Salient Object Detection and Beyond
Keren Fu, Deng-Ping Fan, Ge-Peng Ji +3
Existing RGB-D salient object detection (SOD) models usually treat RGB and depth as independent information and design separate networks for feature extraction from each. Such sche…
RGB-D Salient Object Detection: A Survey
Tao Zhou, Deng-Ping Fan, Ming-Ming Cheng +2
Salient object detection (SOD), which simulates the human visual perception system to locate the most attractive object(s) in a scene, has been widely applied to various computer v…
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…
Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification
Mang Ye, Jianbing Shen, David J. Crandall +2
Visible-infrared person re-identification (VI-ReID) is a challenging cross-modality pedestrian retrieval problem. Due to the large intra-class variations and cross-modality discrep…
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…
Re-thinking Co-Salient Object Detection
Deng-Ping Fan, Tengpeng Li, Zheng Lin +5
In this paper, we conduct a comprehensive study on the co-salient object detection (CoSOD) problem for images. CoSOD is an emerging and rapidly growing extension of salient object…
Self-Learning with Rectification Strategy for Human Parsing
Tao Li, Zhiyuan Liang, Sanyuan Zhao +2
In this paper, we solve the sample shortage problem in the human parsing task. We begin with the self-learning strategy, which generates pseudo-labels for unlabeled data to retrain…
LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention
Junbo Yin, Jianbing Shen, Chenye Guan +2
Existing LiDAR-based 3D object detectors usually focus on the single-frame detection, while ignoring the spatiotemporal information in consecutive point cloud frames. In this paper…
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…
Motion-Attentive Transition for Zero-Shot Video Object Segmentation
Tianfei Zhou, Shunzhou Wang, Yi Zhou +3
In this paper, we present a novel Motion-Attentive Transition Network (MATNet) for zero-shot video object segmentation, which provides a new way of leveraging motion information to…
Hi-Net: Hybrid-fusion Network for Multi-modal MR Image Synthesis
Tao Zhou, Huazhu Fu, Geng Chen +2
Magnetic resonance imaging (MRI) is a widely used neuroimaging technique that can provide images of different contrasts (i.e., modalities). Fusing this multi-modal data has proven…
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…
NETNet: Neighbor Erasing and Transferring Network for Better Single Shot Object Detection
Yazhao Li, Yanwei Pang, Jianbing Shen +2
Due to the advantages of real-time detection and improved performance, single-shot detectors have gained great attention recently. To solve the complex scale variations, single-sho…
Deep Learning for Person Re-identification: A Survey and Outlook
Mang Ye, Jianbing Shen, Gaojie Lin +3
Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-overlapping cameras. With the advancement of deep neural networks and increasing demand…