115 citations · 775 across the 83 of their papers we have counts for
19 papers · 1 filter
Towards Spatio-Temporal Video Scene Text Detection via Temporal Clustering
Yuanqiang Cai, Chang Liu, Weiqiang Wang +1
With only bounding-box annotations in the spatial domain, existing video scene text detection (VSTD) benchmarks lack temporal relation of text instances among video frames, which h…
Adaptive Linear Span Network for Object Skeleton Detection
Chang Liu, Yunjie Tian, Jianbin Jiao +1
Conventional networks for object skeleton detection are usually hand-crafted. Although effective, they require intensive priori knowledge to configure representative features for o…
The 1st Tiny Object Detection Challenge:Methods and Results
Xuehui Yu, Zhenjun Han, Yuqi Gong +22
The 1st Tiny Object Detection (TOD) Challenge aims to encourage research in developing novel and accurate methods for tiny object detection in images which have wide views, with a…
Prototype Mixture Models for Few-shot Semantic Segmentation
Boyu Yang, Chang Liu, Bohao Li +2
Few-shot segmentation is challenging because objects within the support and query images could significantly differ in appearance and pose. Using a single prototype acquired direct…
Component Divide-and-Conquer for Real-World Image Super-Resolution
Pengxu Wei, Ziwei Xie, Hannan Lu +4
In this paper, we present a large-scale Diverse Real-world image Super-Resolution dataset, i.e., DRealSR, as well as a divide-and-conquer Super-Resolution (SR) network, exploring t…
Learning Task-oriented Disentangled Representations for Unsupervised Domain Adaptation
Pingyang Dai, Peixian Chen, Qiong Wu +4
Unsupervised domain adaptation (UDA) aims to address the domain-shift problem between a labeled source domain and an unlabeled target domain. Many efforts have been made to address…
Progressive Cluster Purification for Unsupervised Feature Learning
Yifei Zhang, Chang Liu, Yu Zhou +3
In unsupervised feature learning, sample specificity based methods ignore the inter-class information, which deteriorates the discriminative capability of representation models. Cl…
Multiple Expert Brainstorming for Domain Adaptive Person Re-identification
Yunpeng Zhai, Qixiang Ye, Shijian Lu +3
Often the best performing deep neural models are ensembles of multiple base-level networks, nevertheless, ensemble learning with respect to domain adaptive person re-ID remains une…
Discretization-Aware Architecture Search
Yunjie Tian, Chang Liu, Lingxi Xie +2
The search cost of neural architecture search (NAS) has been largely reduced by weight-sharing methods. These methods optimize a super-network with all possible edges and operation…
Domain Contrast for Domain Adaptive Object Detection
Feng Liu, Xiaoxong Zhang, Fang Wan +2
We present Domain Contrast (DC), a simple yet effective approach inspired by contrastive learning for training domain adaptive detectors. DC is deduced from the error bound minimiz…
iffDetector: Inference-aware Feature Filtering for Object Detection
Mingyuan Mao, Yuxin Tian, Baochang Zhang +4
Modern CNN-based object detectors focus on feature configuration during training but often ignore feature optimization during inference. In this paper, we propose a new feature opt…
Video Playback Rate Perception for Self-supervisedSpatio-Temporal Representation Learning
Yuan Yao, Chang Liu, Dezhao Luo +2
In self-supervised spatio-temporal representation learning, the temporal resolution and long-short term characteristics are not yet fully explored, which limits representation capa…
Cogradient Descent for Bilinear Optimization
Li'an Zhuo, Baochang Zhang, Linlin Yang +5
Conventional learning methods simplify the bilinear model by regarding two intrinsically coupled factors independently, which degrades the optimization procedure. One reason lies i…
Rethinking Performance Estimation in Neural Architecture Search
Xiawu Zheng, Rongrong Ji, Qiang Wang +4
Neural architecture search (NAS) remains a challenging problem, which is attributed to the indispensable and time-consuming component of performance estimation (PE). In this paper,…
AD-Cluster: Augmented Discriminative Clustering for Domain Adaptive Person Re-identification
Yunpeng Zhai, Shijian Lu, Qixiang Ye +4
Domain adaptive person re-identification (re-ID) is a challenging task, especially when person identities in target domains are unknown. Existing methods attempt to address this ch…
Self-Guided Adaptation: Progressive Representation Alignment for Domain Adaptive Object Detection
Zongxian Li, Qixiang Ye, Chong Zhang +3
Unsupervised domain adaptation (UDA) has achieved unprecedented success in improving the cross-domain robustness of object detection models. However, existing UDA methods largely i…
Architecture Disentanglement for Deep Neural Networks
Jie Hu, Liujuan Cao, Qixiang Ye +6
Understanding the inner workings of deep neural networks (DNNs) is essential to provide trustworthy artificial intelligence techniques for practical applications. Existing studies…
Video Cloze Procedure for Self-Supervised Spatio-Temporal Learning
Dezhao Luo, Chang Liu, Yu Zhou +4
We propose a novel self-supervised method, referred to as Video Cloze Procedure (VCP), to learn rich spatial-temporal representations. VCP first generates "blanks" by withholding v…
Filter Sketch for Network Pruning
Mingbao Lin, Liujuan Cao, Shaojie Li +5
We propose a novel network pruning approach by information preserving of pre-trained network weights (filters). Network pruning with the information preserving is formulated as a m…