67 citations · 969 across the 81 of their papers we have counts for
22 papers · 1 filter
Frequency Consistent Adaptation for Real World Super Resolution
Xiaozhong Ji, Guangpin Tao, Yun Cao +5
Recent deep-learning based Super-Resolution (SR) methods have achieved remarkable performance on images with known degradation. However, these methods always fail in real-world sce…
Effective Label Propagation for Discriminative Semi-Supervised Domain Adaptation
Zhiyong Huang, Kekai Sheng, Weiming Dong +5
Semi-supervised domain adaptation (SSDA) methods have demonstrated great potential in large-scale image classification tasks when massive labeled data are available in the source d…
Fast Class-wise Updating for Online Hashing
Mingbao Lin, Rongrong Ji, Xiaoshuai Sun +4
Online image hashing has received increasing research attention recently, which processes large-scale data in a streaming fashion to update the hash functions on-the-fly. To this e…
Adversarial Refinement Network for Human Motion Prediction
Xianjin Chao, Yanrui Bin, Wenqing Chu +6
Human motion prediction aims to predict future 3D skeletal sequences by giving a limited human motion as inputs. Two popular methods, recurrent neural networks and feed-forward dee…
Rotated Binary Neural Network
Mingbao Lin, Rongrong Ji, Zihan Xu +5
Binary Neural Network (BNN) shows its predominance in reducing the complexity of deep neural networks. However, it suffers severe performance degradation. One of the major impedime…
Devil's in the Details: Aligning Visual Clues for Conditional Embedding in Person Re-Identification
Fufu Yu, Xinyang Jiang, Yifei Gong +5
Although Person Re-Identification has made impressive progress, difficult cases like occlusion, change of view-pointand similar clothing still bring great challenges. Besides overa…
Face Anti-Spoofing Via Disentangled Representation Learning
Ke-Yue Zhang, Taiping Yao, Jian Zhang +6
Face anti-spoofing is crucial to security of face recognition systems. Previous approaches focus on developing discriminative models based on the features extracted from images, wh…
Adversarial Semantic Data Augmentation for Human Pose Estimation
Yanrui Bin, Xuan Cao, Xinya Chen +7
Human pose estimation is the task of localizing body keypoints from still images. The state-of-the-art methods suffer from insufficient examples of challenging cases such as symmet…
Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking
Jinlong Peng, Changan Wang, Fangbin Wan +7
Existing Multiple-Object Tracking (MOT) methods either follow the tracking-by-detection paradigm to conduct object detection, feature extraction and data association separately, or…
NOH-NMS: Improving Pedestrian Detection by Nearby Objects Hallucination
Penghao Zhou, Chong Zhou, Pai Peng +4
Greedy-NMS inherently raises a dilemma, where a lower NMS threshold will potentially lead to a lower recall rate and a higher threshold introduces more false positives. This proble…
Temporal Distinct Representation Learning for Action Recognition
Junwu Weng, Donghao Luo, Yabiao Wang +6
Motivated by the previous success of Two-Dimensional Convolutional Neural Network (2D CNN) on image recognition, researchers endeavor to leverage it to characterize videos. However…
Collaborative Learning for Faster StyleGAN Embedding
Shanyan Guan, Ying Tai, Bingbing Ni +3
The latent code of the recent popular model StyleGAN has learned disentangled representations thanks to the multi-layer style-based generator. Embedding a given image back to the l…
ACFD: Asymmetric Cartoon Face Detector
Bin Zhang, Jian Li, Yabiao Wang +5
Cartoon face detection is a more challenging task than human face detection due to many difficult scenarios is involved. Aiming at the characteristics of cartoon faces, such as hug…
Dense Scene Multiple Object Tracking with Box-Plane Matching
Jinlong Peng, Yueyang Gu, Yabiao Wang +3
Multiple Object Tracking (MOT) is an important task in computer vision. MOT is still challenging due to the occlusion problem, especially in dense scenes. Following the tracking-by…
Arbitrary Style Transfer via Multi-Adaptation Network
Yingying Deng, Fan Tang, Weiming Dong +3
Arbitrary style transfer is a significant topic with research value and application prospect. A desired style transfer, given a content image and referenced style painting, would r…
Filter Grafting for Deep Neural Networks: Reason, Method, and Cultivation
Hao Cheng, Fanxu Meng, Ke Li +4
Filter is the key component in modern convolutional neural networks (CNNs). However, since CNNs are usually over-parameterized, a pre-trained network always contain some invalid (u…
CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition
Yuge Huang, Yuhan Wang, Ying Tai +5
As an emerging topic in face recognition, designing margin-based loss functions can increase the feature margin between different classes for enhanced discriminability. More recent…
ASFD: Automatic and Scalable Face Detector
Bin Zhang, Jian Li, Yabiao Wang +7
In this paper, we propose a novel Automatic and Scalable Face Detector (ASFD), which is based on a combination of neural architecture search techniques as well as a new loss design…
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…
Towards Palmprint Verification On Smartphones
Yingyi Zhang, Lin Zhang, Ruixin Zhang +3
With the rapid development of mobile devices, smartphones have gradually become an indispensable part of people's lives. Meanwhile, biometric authentication has been corroborated t…
Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimation
Liang Liu, Jiangning Zhang, Ruifei He +7
Unsupervised learning of optical flow, which leverages the supervision from view synthesis, has emerged as a promising alternative to supervised methods. However, the objective of…
Improving Face Recognition from Hard Samples via Distribution Distillation Loss
Yuge Huang, Pengcheng Shen, Ying Tai +5
Large facial variations are the main challenge in face recognition. To this end, previous variation-specific methods make full use of task-related prior to design special network l…