42 citations · 70 across the 13 of their papers we have counts for
6 papers · 1 filter
Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation
Hang Wang, Minghao Xu, Bingbing Ni +1
Transferring knowledges learned from multiple source domains to target domain is a more practical and challenging task than conventional single-source domain adaptation. Furthermor…
Cross-domain Detection via Graph-induced Prototype Alignment
Minghao Xu, Hang Wang, Bingbing Ni +2
Applying the knowledge of an object detector trained on a specific domain directly onto a new domain is risky, as the gap between two domains can severely degrade model's performan…
Adversarial Domain Adaptation with Domain Mixup
Minghao Xu, Jian Zhang, Bingbing Ni +4
Recent works on domain adaptation reveal the effectiveness of adversarial learning on filling the discrepancy between source and target domains. However, two common limitations exi…
Towards Locally Consistent Object Counting with Constrained Multi-stage Convolutional Neural Networks
Muming Zhao, Jian Zhang, Chongyang Zhang +1
High-density object counting in surveillance scenes is challenging mainly due to the drastic variation of object scales. The prevalence of deep learning has largely boosted the obj…
Online Multi-Object Tracking with Dual Matching Attention Networks
Ji Zhu, Hua Yang, Nian Liu +3
In this paper, we propose an online Multi-Object Tracking (MOT) approach which integrates the merits of single object tracking and data association methods in a unified framework t…
Multi-Scale Spatially-Asymmetric Recalibration for Image Classification
Yan Wang, Lingxi Xie, Siyuan Qiao +3
Convolution is spatially-symmetric, i.e., the visual features are independent of its position in the image, which limits its ability to utilize contextual cues for visual recogniti…