149 citations · 675 across the 60 of their papers we have counts for
12 papers · 1 filter
Divide and Adapt: Active Domain Adaptation via Customized Learning
Duojun Huang, Jichang Li, Weikai Chen +3
Active domain adaptation (ADA) aims to improve the model adaptation performance by incorporating active learning (AL) techniques to label a maximally-informative subset of target s…
Exploration and Exploitation of Unlabeled Data for Open-Set Semi-Supervised Learning
Ganlong Zhao, Guanbin Li, Yipeng Qin +5
In this paper, we address a complex but practical scenario in semi-supervised learning (SSL) named open-set SSL, where unlabeled data contain both in-distribution (ID) and out-of-d…
Towards Accurate Post-Training Quantization for Vision Transformer
Yifu Ding, Haotong Qin, Qinghua Yan +4
Vision transformer emerges as a potential architecture for vision tasks. However, the intense computation and non-negligible delay hinder its application in the real world. As a wi…
DRKF: Distilled Rotated Kernel Fusion for Efficient Rotation Invariant Descriptors in Local Feature Matching
Ranran Huang, Jiancheng Cai, Chao Li +3
The performance of local feature descriptors degrades in the presence of large rotation variations. To address this issue, we present an efficient approach to learning rotation inv…
Contrastive Attention Network with Dense Field Estimation for Face Completion
Xin Ma, Xiaoqiang Zhou, Huaibo Huang +3
Most modern face completion approaches adopt an autoencoder or its variants to restore missing regions in face images. Encoders are often utilized to learn powerful representations…
Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for Open-Set Semi-Supervised Learning
Junkai Huang, Chaowei Fang, Weikai Chen +5
Open-set semi-supervised learning (open-set SSL) investigates a challenging but practical scenario where out-of-distribution (OOD) samples are contained in the unlabeled data. Whil…
Rethinking BiSeNet For Real-time Semantic Segmentation
Mingyuan Fan, Shenqi Lai, Junshi Huang +4
BiSeNet has been proved to be a popular two-stream network for real-time segmentation. However, its principle of adding an extra path to encode spatial information is time-consumin…
Feature Decomposition and Reconstruction Learning for Effective Facial Expression Recognition
Delian Ruan, Yan Yan, Shenqi Lai +3
In this paper, we propose a novel Feature Decomposition and Reconstruction Learning (FDRL) method for effective facial expression recognition. We view the expression information as…
Free-Form Image Inpainting via Contrastive Attention Network
Xin Ma, Xiaoqiang Zhou, Huaibo Huang +3
Most deep learning based image inpainting approaches adopt autoencoder or its variants to fill missing regions in images. Encoders are usually utilized to learn powerful representa…
Query Twice: Dual Mixture Attention Meta Learning for Video Summarization
Junyan Wang, Yang Bai, Yang Long +4
Video summarization aims to select representative frames to retain high-level information, which is usually solved by predicting the segment-wise importance score via a softmax fun…
Reference-guided Face Component Editing
Qiyao Deng, Jie Cao, Yunfan Liu +3
Face portrait editing has achieved great progress in recent years. However, previous methods either 1) operate on pre-defined face attributes, lacking the flexibility of controllin…
Grand Challenge of 106-Point Facial Landmark Localization
Yinglu Liu, Hao Shen, Yue Si +18
Facial landmark localization is a very crucial step in numerous face related applications, such as face recognition, facial pose estimation, face image synthesis, etc. However, pre…