67 citations · 969 across the 81 of their papers we have counts for
17 papers · 1 filter
Learning Semantic Neural Tree for Human Parsing
Ruyi Ji, Dawei Du, Libo Zhang +5
The majority of existing human parsing methods formulate the task as semantic segmentation, which regard each semantic category equally and fail to exploit the intrinsic physiologi…
Asymmetric Co-Teaching for Unsupervised Cross Domain Person Re-Identification
Fengxiang Yang, Ke Li, Zhun Zhong +7
Person re-identification (re-ID), is a challenging task due to the high variance within identity samples and imaging conditions. Although recent advances in deep learning have achi…
Viewpoint-Aware Loss with Angular Regularization for Person Re-Identification
Zhihui Zhu, Xinyang Jiang, Feng Zheng +4
Although great progress in supervised person re-identification (Re-ID) has been made recently, due to the viewpoint variation of a person, Re-ID remains a massive visual challenge.…
Rethinking Temporal Fusion for Video-based Person Re-identification on Semantic and Time Aspect
Xinyang Jiang, Yifei Gong, Xiaowei Guo +5
Recently, the research interest of person re-identification (ReID) has gradually turned to video-based methods, which acquire a person representation by aggregating frame features…
Revisiting Image Aesthetic Assessment via Self-Supervised Feature Learning
Kekai Sheng, Weiming Dong, Menglei Chai +6
Visual aesthetic assessment has been an active research field for decades. Although latest methods have achieved promising performance on benchmark datasets, they typically rely on…
TEINet: Towards an Efficient Architecture for Video Recognition
Zhaoyang Liu, Donghao Luo, Yabiao Wang +6
Efficiency is an important issue in designing video architectures for action recognition. 3D CNNs have witnessed remarkable progress in action recognition from videos. However, com…
Fast Learning of Temporal Action Proposal via Dense Boundary Generator
Chuming Lin, Jian Li, Yabiao Wang +7
Generating temporal action proposals remains a very challenging problem, where the main issue lies in predicting precise temporal proposal boundaries and reliable action confidence…
Semantic-aware Image Deblurring
Fuhai Chen, Rongrong Ji, Chengpeng Dai +6
Image deblurring has achieved exciting progress in recent years. However, traditional methods fail to deblur severely blurred images, where semantic contents appears ambiguously. I…
Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization
Ruyi Ji, Longyin Wen, Libo Zhang +5
Fine-grained visual categorization (FGVC) is an important but challenging task due to high intra-class variances and low inter-class variances caused by deformation, occlusion, ill…
Semi-Supervised Adversarial Monocular Depth Estimation
Rongrong Ji, Ke Li, Yan Wang +6
In this paper, we address the problem of monocular depth estimation when only a limited number of training image-depth pairs are available. To achieve a high regression accuracy, t…
Scale Invariant Fully Convolutional Network: Detecting Hands Efficiently
Dan Liu, Dawei Du, Libo Zhang +4
Existing hand detection methods usually follow the pipeline of multiple stages with high computation cost, i.e., feature extraction, region proposal, bounding box regression, and a…
Interpretable Neural Network Decoupling
Yuchao Li, Rongrong Ji, Shaohui Lin +5
The remarkable performance of convolutional neural networks (CNNs) is entangled with their huge number of uninterpretable parameters, which has become the bottleneck limiting the e…
Supervised Online Hashing via Similarity Distribution Learning
Mingbao Lin, Rongrong Ji, Shen Chen +6
Online hashing has attracted extensive research attention when facing streaming data. Most online hashing methods, learning binary codes based on pairwise similarities of training…
Anti-Confusing: Region-Aware Network for Human Pose Estimation
Xuan Cao, Yanhao Ge, Ying Tai +5
In this work, we propose a novel framework named Region-Aware Network (RANet), which learns the ability of anti-confusing in case of heavy occlusion, nearby person and symmetric ap…
LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning
Huaiyu Li, Weiming Dong, Xing Mei +3
In this work, we propose a novel meta-learning approach for few-shot classification, which learns transferable prior knowledge across tasks and directly produces network parameters…
Towards Optimal Structured CNN Pruning via Generative Adversarial Learning
Shaohui Lin, Rongrong Ji, Chenqian Yan +5
Structured pruning of filters or neurons has received increased focus for compressing convolutional neural networks. Most existing methods rely on multi-stage optimizations in a la…
Aurora Guard: Real-Time Face Anti-Spoofing via Light Reflection
Yao Liu, Ying Tai, Jilin Li +6
In this paper, we propose a light reflection based face anti-spoofing method named Aurora Guard (AG), which is fast, simple yet effective that has already been deployed in real-wor…