388 citations · 1.3k across the 31 of their papers we have counts for
31 papers
Calibrating Multimodal Learning
Huan Ma. Qingyang Zhang, Changqing Zhang, Bingzhe Wu +3
Multimodal machine learning has achieved remarkable progress in a wide range of scenarios. However, the reliability of multimodal learning remains largely unexplored. In this paper…
Multi-Granularity Regularized Re-Balancing for Class Incremental Learning
Huitong Chen, Yu Wang, Qinghua Hu
Deep learning models suffer from catastrophic forgetting when learning new tasks incrementally. Incremental learning has been proposed to retain the knowledge of old classes while…
Learning Self-Supervised Low-Rank Network for Single-Stage Weakly and Semi-Supervised Semantic Segmentation
Junwen Pan, Pengfei Zhu, Kaihua Zhang +5
Semantic segmentation with limited annotations, such as weakly supervised semantic segmentation (WSSS) and semi-supervised semantic segmentation (SSSS), is a challenging task that…
Uncertainty-Aware Multi-View Representation Learning
Yu Geng, Zongbo Han, Changqing Zhang +1
Learning from different data views by exploring the underlying complementary information among them can endow the representation with stronger expressive ability. However, high-dim…
Learning Dynamic Compact Memory Embedding for Deformable Visual Object Tracking
Pengfei Zhu, Hongtao Yu, Kaihua Zhang +5
Recently, template-based trackers have become the leading tracking algorithms with promising performance in terms of efficiency and accuracy. However, the correlation operation bet…
Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributions
Huan Ma, Zongbo Han, Changqing Zhang +3
Multimodal regression is a fundamental task, which integrates the information from different sources to improve the performance of follow-up applications. However, existing methods…
Temporal-attentive Covariance Pooling Networks for Video Recognition
Zilin Gao, Qilong Wang, Bingbing Zhang +2
For video recognition task, a global representation summarizing the whole contents of the video snippets plays an important role for the final performance. However, existing video…
T-SVDNet: Exploring High-Order Prototypical Correlations for Multi-Source Domain Adaptation
Ruihuang Li, Xu Jia, Jianzhong He +2
Most existing domain adaptation methods focus on adaptation from only one source domain, however, in practice there are a number of relevant sources that could be leveraged to help…
VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results
Dawei Du, Longyin Wen, Pengfei Zhu +52
Crowd counting on the drone platform is an interesting topic in computer vision, which brings new challenges such as small object inference, background clutter and wide viewpoint.…
Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark
Longyin Wen, Dawei Du, Pengfei Zhu +4
To promote the developments of object detection, tracking and counting algorithms in drone-captured videos, we construct a benchmark with a new drone-captured largescale dataset, n…
Learning Class-Agnostic Pseudo Mask Generation for Box-Supervised Semantic Segmentation
Chaohao Xie, Dongwei Ren, Lei Wang +1
Recently, several weakly supervised learning methods have been devoted to utilize bounding box supervision for training deep semantic segmentation models. Most existing methods usu…
Deep Partial Multi-View Learning
Changqing Zhang, Yajie Cui, Zongbo Han +3
Although multi-view learning has made signifificant progress over the past few decades, it is still challenging due to the diffificulty in modeling complex correlations among diffe…
SPL-MLL: Selecting Predictable Landmarks for Multi-Label Learning
Junbing Li, Changqing Zhang, Pengfei Zhu +3
Although significant progress achieved, multi-label classification is still challenging due to the complexity of correlations among different labels. Furthermore, modeling the rela…
Expert Training: Task Hardness Aware Meta-Learning for Few-Shot Classification
Yucan Zhou, Yu Wang, Jianfei Cai +3
Deep neural networks are highly effective when a large number of labeled samples are available but fail with few-shot classification tasks. Recently, meta-learning methods have rec…
Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation
Zhaohui Zheng, Ping Wang, Dongwei Ren +4
Deep learning-based object detection and instance segmentation have achieved unprecedented progress. In this paper, we propose Complete-IoU (CIoU) loss and Cluster-NMS for enhancin…
What Deep CNNs Benefit from Global Covariance Pooling: An Optimization Perspective
Qilong Wang, Li Zhang, Banggu Wu +4
Recent works have demonstrated that global covariance pooling (GCP) has the ability to improve performance of deep convolutional neural networks (CNNs) on visual classification tas…
Multi-Drone based Single Object Tracking with Agent Sharing Network
Pengfei Zhu, Jiayu Zheng, Dawei Du +3
Drone equipped with cameras can dynamically track the target in the air from a broader view compared with static cameras or moving sensors over the ground. However, it is still cha…
Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning
Yiming Sun, Bing Cao, Pengfei Zhu +1
Drone-based vehicle detection aims at finding the vehicle locations and categories in an aerial image. It empowers smart city traffic management and disaster rescue. Researchers ha…
Detection and Tracking Meet Drones Challenge
Pengfei Zhu, Longyin Wen, Dawei Du +4
Drones, or general UAVs, equipped with cameras have been fast deployed with a wide range of applications, including agriculture, aerial photography, and surveillance. Consequently,…
Drone-based Joint Density Map Estimation, Localization and Tracking with Space-Time Multi-Scale Attention Network
Longyin Wen, Dawei Du, Pengfei Zhu +4
This paper proposes a space-time multi-scale attention network (STANet) to solve density map estimation, localization and tracking in dense crowds of video clips captured by drones…
ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
Qilong Wang, Banggu Wu, Pengfei Zhu +3
Recently, channel attention mechanism has demonstrated to offer great potential in improving the performance of deep convolutional neural networks (CNNs). However, most existing me…
Neural Blind Deconvolution Using Deep Priors
Dongwei Ren, Kai Zhang, Qilong Wang +2
Blind deconvolution is a classical yet challenging low-level vision problem with many real-world applications. Traditional maximum a posterior (MAP) based methods rely heavily on f…
Multi-view Deep Subspace Clustering Networks
Pengfei Zhu, Xinjie Yao, Yu Wang +3
Multi-view subspace clustering aims to discover the inherent structure of data by fusing multiple views of complementary information. Most existing methods first extract multiple t…
Semi-interactive Attention Network for Answer Understanding in Reverse-QA
Qing Yin, Guan Luo, Xiaodong Zhu +2
Question answering (QA) is an important natural language processing (NLP) task and has received much attention in academic research and industry communities. Existing QA studies as…