71 citations · 225 across the 30 of their papers we have counts for
26 papers · 1 filter
Quadric Representations for LiDAR Odometry, Mapping and Localization
Chao Xia, Chenfeng Xu, Patrick Rim +5
Current LiDAR odometry, mapping and localization methods leverage point-wise representations of 3D scenes and achieve high accuracy in autonomous driving tasks. However, the space-…
Tightly Coupled Learning Strategy for Weakly Supervised Hierarchical Place Recognition
Y. Shen, R. Wang, W. Zuo +1
Visual place recognition (VPR) is a key issue for robotics and autonomous systems. For the trade-off between time and performance, most of methods use the coarse-to-fine hierarchic…
LiVLR: A Lightweight Visual-Linguistic Reasoning Framework for Video Question Answering
Jingjing Jiang, Ziyi Liu, Nanning Zheng
Video Question Answering (VideoQA), aiming to correctly answer the given question based on understanding multi-modal video content, is challenging due to the rich video content. Fr…
Instance-Conditional Knowledge Distillation for Object Detection
Zijian Kang, Peizhen Zhang, Xiangyu Zhang +2
Knowledge distillation has shown great success in classification, however, it is still challenging for detection. In a typical image for detection, representations from different l…
LGD: Label-guided Self-distillation for Object Detection
Peizhen Zhang, Zijian Kang, Tong Yang +3
In this paper, we propose the first self-distillation framework for general object detection, termed LGD (Label-Guided self-Distillation). Previous studies rely on a strong pretrai…
Enriching Local and Global Contexts for Temporal Action Localization
Zixin Zhu, Wei Tang, Le Wang +2
Effectively tackling the problem of temporal action localization (TAL) necessitates a visual representation that jointly pursues two confounding goals, i.e., fine-grained discrimin…
X-GGM: Graph Generative Modeling for Out-of-Distribution Generalization in Visual Question Answering
Jingjing Jiang, Ziyi Liu, Yifan Liu +2
Encouraging progress has been made towards Visual Question Answering (VQA) in recent years, but it is still challenging to enable VQA models to adaptively generalize to out-of-dist…
Adversarial Attack and Defense in Deep Ranking
Mo Zhou, Le Wang, Zhenxing Niu +3
Deep Neural Network classifiers are vulnerable to adversarial attack, where an imperceptible perturbation could result in misclassification. However, the vulnerability of DNN-based…
Video Imprint
Zhanning Gao, Le Wang, Nebojsa Jojic +3
A new unified video analytics framework (ER3) is proposed for complex event retrieval, recognition and recounting, based on the proposed video imprint representation, which exploit…
Weakly Supervised Temporal Action Localization Through Learning Explicit Subspaces for Action and Context
Ziyi Liu, Le Wang, Wei Tang +3
Weakly-supervised Temporal Action Localization (WS-TAL) methods learn to localize temporal starts and ends of action instances in a video under only video-level supervision. Existi…
ACSNet: Action-Context Separation Network for Weakly Supervised Temporal Action Localization
Ziyi Liu, Le Wang, Qilin Zhang +4
The object of Weakly-supervised Temporal Action Localization (WS-TAL) is to localize all action instances in an untrimmed video with only video-level supervision. Due to the lack o…
Fine-Grained Dynamic Head for Object Detection
Lin Song, Yanwei Li, Zhengkai Jiang +4
The Feature Pyramid Network (FPN) presents a remarkable approach to alleviate the scale variance in object representation by performing instance-level assignments. Nevertheless, th…
Rethinking Learnable Tree Filter for Generic Feature Transform
Lin Song, Yanwei Li, Zhengkai Jiang +5
The Learnable Tree Filter presents a remarkable approach to model structure-preserving relations for semantic segmentation. Nevertheless, the intrinsic geometric constraint forces…
End-to-End Object Detection with Fully Convolutional Network
Jianfeng Wang, Lin Song, Zeming Li +3
Mainstream object detectors based on the fully convolutional network has achieved impressive performance. While most of them still need a hand-designed non-maximum suppression (NMS…
Learning to Infer Unseen Single-/Multi-Attribute-Object Compositions with Graph Networks
Hui Chen, Jingjing Jiang, Nanning Zheng
Inferring the unseen attribute-object composition is critical to make machines learn to decompose and compose complex concepts like people. Most existing methods are limited to the…
Discriminative Feature Learning with Foreground Attention for Person Re-Identification
Sanping Zhou, Jinjun Wang, Deyu Meng +3
The performance of person re-identification (Re-ID) has been seriously effected by the large cross-view appearance variations caused by mutual occlusions and background clutters. H…
Attention-based Temporal Weighted Convolutional Neural Network for Action Recognition
Jinliang Zang, Le Wang, Ziyi Liu +4
Research in human action recognition has accelerated significantly since the introduction of powerful machine learning tools such as Convolutional Neural Networks (CNNs). However,…
Feature Selective Small Object Detection via Knowledge-based Recurrent Attentive Neural Network
Kai Yi, Zhiqiang Jian, Shitao Chen +1
At present, the performance of deep neural network in general object detection is comparable to or even surpasses that of human beings. However, due to the limitations of deep lear…
Deep Self-Paced Learning for Person Re-Identification
Sanping Zhou, Jinjun Wang, Deyu Meng +4
Person re-identification (Re-ID) usually suffers from noisy samples with background clutter and mutual occlusion, which makes it extremely difficult to distinguish different indivi…
Large Margin Learning in Set to Set Similarity Comparison for Person Re-identification
Sanping Zhou, Jinjun Wang, Rui Shi +3
Person re-identification (Re-ID) aims at matching images of the same person across disjoint camera views, which is a challenging problem in multimedia analysis, multimedia editing…
Deep Feature Learning via Structured Graph Laplacian Embedding for Person Re-Identification
De Cheng, Yihong Gong, Zhihui Li +3
Learning the distance metric between pairs of examples is of great importance for visual recognition, especially for person re-identification (Re-Id). Recently, the contrastive and…
Detecting Drivable Area for Self-driving Cars: An Unsupervised Approach
Ziyi Liu, Siyu Yu, Xiao Wang +1
It has been well recognized that detecting drivable area is central to self-driving cars. Most of existing methods attempt to locate road surface by using lane line, thereby restri…
Single Image Super Resolution - When Model Adaptation Matters
Yudong Liang, Radu Timofte, Jinjun Wang +2
In the recent years impressive advances were made for single image super-resolution. Deep learning is behind a big part of this success. Deep(er) architecture design and external p…
Single Image Super-resolution via a Lightweight Residual Convolutional Neural Network
Yudong Liang, Ze Yang, Kai Zhang +3
Recent years have witnessed great success of convolutional neural network (CNN) for various problems both in low and high level visions. Especially noteworthy is the residual netwo…