no code implementations • 29 Nov 2023 • Weixin Mao, Tiancai Wang, Diankun Zhang, Junjie Yan, Osamu Yoshie
Pillar-based methods mainly employ randomly initialized 2D convolution neural network (ConvNet) for feature extraction and fail to enjoy the benefits from the backbone scaling and pretraining in the image domain.
2 code implementations • ICCV 2023 • Junjie Yan, Yingfei Liu, Jianjian Sun, Fan Jia, Shuailin Li, Tiancai Wang, Xiangyu Zhang
In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection.
1 code implementation • ICCV 2023 • Yingfei Liu, Junjie Yan, Fan Jia, Shuailin Li, Aqi Gao, Tiancai Wang, Xiangyu Zhang, Jian Sun
More specifically, we extend the 3D position embedding (3D PE) in PETR for temporal modeling.
Ranked #2 on Bird's-Eye View Semantic Segmentation on nuScenes (IoU lane - 224x480 - 100x100 at 0.5 metric)
3 code implementations • ICLR 2022 • Zhen Qin, Weixuan Sun, Hui Deng, Dongxu Li, Yunshen Wei, Baohong Lv, Junjie Yan, Lingpeng Kong, Yiran Zhong
As one of its core components, the softmax attention helps to capture long-range dependencies yet prohibits its scale-up due to the quadratic space and time complexity to the sequence length.
Ranked #4 on Offline RL on D4RL
no code implementations • 24 Nov 2021 • Yujie Wang, Junqin Huang, Mengya Gao, Yichao Wu, Zhenfei Yin, Ding Liang, Junjie Yan
Transferring with few data in a general way to thousands of downstream tasks is becoming a trend of the foundation model's application.
no code implementations • 16 Nov 2021 • Jing Shao, Siyu Chen, Yangguang Li, Kun Wang, Zhenfei Yin, Yinan He, Jianing Teng, Qinghong Sun, Mengya Gao, Jihao Liu, Gengshi Huang, Guanglu Song, Yichao Wu, Yuming Huang, Fenggang Liu, Huan Peng, Shuo Qin, Chengyu Wang, Yujie Wang, Conghui He, Ding Liang, Yu Liu, Fengwei Yu, Junjie Yan, Dahua Lin, Xiaogang Wang, Yu Qiao
Enormous waves of technological innovations over the past several years, marked by the advances in AI technologies, are profoundly reshaping the industry and the society.
1 code implementation • 5 Nov 2021 • Yuhang Li, Mingzhu Shen, Jian Ma, Yan Ren, Mingxin Zhao, Qi Zhang, Ruihao Gong, Fengwei Yu, Junjie Yan
Surprisingly, no existing algorithm wins every challenge in MQBench, and we hope this work could inspire future research directions.
3 code implementations • ICLR 2022 • Yangguang Li, Feng Liang, Lichen Zhao, Yufeng Cui, Wanli Ouyang, Jing Shao, Fengwei Yu, Junjie Yan
Recently, large-scale Contrastive Language-Image Pre-training (CLIP) has attracted unprecedented attention for its impressive zero-shot recognition ability and excellent transferability to downstream tasks.
1 code implementation • ICCV 2021 • BoYu Chen, Peixia Li, Baopu Li, Chen Lin, Chuming Li, Ming Sun, Junjie Yan, Wanli Ouyang
We present BN-NAS, neural architecture search with Batch Normalization (BN-NAS), to accelerate neural architecture search (NAS).
no code implementations • 7 Aug 2021 • BoYu Chen, Peixia Li, Baopu Li, Chuming Li, Lei Bai, Chen Lin, Ming Sun, Junjie Yan, Wanli Ouyang
Then, a compact set of the possible combinations for different token pooling and attention sharing mechanisms are constructed.
1 code implementation • ICCV 2021 • Yan Lu, Xinzhu Ma, Lei Yang, Tianzhu Zhang, Yating Liu, Qi Chu, Junjie Yan, Wanli Ouyang
In this paper, we propose a Geometry Uncertainty Projection Network (GUP Net) to tackle the error amplification problem at both inference and training stages.
3D Object Detection From Monocular Images Depth Estimation +3
2 code implementations • ICCV 2021 • BoYu Chen, Peixia Li, Chuming Li, Baopu Li, Lei Bai, Chen Lin, Ming Sun, Junjie Yan, Wanli Ouyang
We introduce the first Neural Architecture Search (NAS) method to find a better transformer architecture for image recognition.
Ranked #501 on Image Classification on ImageNet
1 code implementation • CVPR 2021 • Xingyuan Bu, Junran Peng, Junjie Yan, Tieniu Tan, Zhaoxiang Zhang
Transfer learning with pre-training on large-scale datasets has played an increasingly significant role in computer vision and natural language processing recently.
no code implementations • 28 May 2021 • Ming Sun, Haoxuan Dou, Baopu Li, Lei Cui, Junjie Yan, Wanli Ouyang
Data sampling acts as a pivotal role in training deep learning models.
no code implementations • ECCV 2020 • Ming Sun, Haoxuan Dou, Junjie Yan
Transfer learning can boost the performance on the targettask by leveraging the knowledge of the source domain.
1 code implementation • CVPR 2021 • Zhiwu Qing, Haisheng Su, Weihao Gan, Dongliang Wang, Wei Wu, Xiang Wang, Yu Qiao, Junjie Yan, Changxin Gao, Nong Sang
In this paper, we propose Temporal Context Aggregation Network (TCANet) to generate high-quality action proposals through "local and global" temporal context aggregation and complementary as well as progressive boundary refinement.
Ranked #9 on Temporal Action Localization on ActivityNet-1.3
no code implementations • 18 Mar 2021 • Jinghao Zhou, Bo Li, Lei Qiao, Peng Wang, Weihao Gan, Wei Wu, Junjie Yan, Wanli Ouyang
Visual Object Tracking (VOT) has synchronous needs for both robustness and accuracy.
no code implementations • 18 Mar 2021 • Jinghao Zhou, Bo Li, Peng Wang, Peixia Li, Weihao Gan, Wei Wu, Junjie Yan, Wanli Ouyang
Visual Object Tracking (VOT) can be seen as an extended task of Few-Shot Learning (FSL).
1 code implementation • CVPR 2021 • Lanyun Zhu, Deyi Ji, Shiping Zhu, Weihao Gan, Wei Wu, Junjie Yan
In this paper, we fully take advantages of the low-level texture features and propose a novel Statistical Texture Learning Network (STLNet) for semantic segmentation.
no code implementations • 2 Mar 2021 • Jiaheng Liu, Yudong Wu, Yichao Wu, Zhenmao Li, Chen Ken, Ding Liang, Junjie Yan
In this study, we make a key observation that the local con-text represented by the similarities between the instance and its inter-class neighbors1plays an important role forFR.
1 code implementation • CVPR 2021 • Jie Liu, Chuming Li, Feng Liang, Chen Lin, Ming Sun, Junjie Yan, Wanli Ouyang, Dong Xu
To develop a practical method for learning complex inception convolution based on the data, a simple but effective search algorithm, referred to as efficient dilation optimization (EDO), is developed.
1 code implementation • 12 Dec 2020 • Matthieu Lin, Chuming Li, Xingyuan Bu, Ming Sun, Chen Lin, Junjie Yan, Wanli Ouyang, Zhidong Deng
Furthermore, the bipartite match of ED harms the training efficiency due to the large ground truth number in crowd scenes.
no code implementations • 8 Dec 2020 • Deyi Ji, Haoran Wang, Hanzhe Hu, Weihao Gan, Wei Wu, Junjie Yan
Most existing re-identification methods focus on learning robust and discriminative features with deep convolution networks.
no code implementations • 2 Nov 2020 • ZiHao Wang, Chen Lin, Lu Sheng, Junjie Yan, Jing Shao
Recently, deep learning has been utilized to solve video recognition problem due to its prominent representation ability.
no code implementations • 21 Oct 2020 • Jie Liu, Chen Lin, Chuming Li, Lu Sheng, Ming Sun, Junjie Yan, Wanli Ouyang
Several variants of stochastic gradient descent (SGD) have been proposed to improve the learning effectiveness and efficiency when training deep neural networks, among which some recent influential attempts would like to adaptively control the parameter-wise learning rate (e. g., Adam and RMSProp).
1 code implementation • ICCV 2021 • Mingzhu Shen, Feng Liang, Ruihao Gong, Yuhang Li, Chuming Li, Chen Lin, Fengwei Yu, Junjie Yan, Wanli Ouyang
Therefore, we propose to combine Network Architecture Search methods with quantization to enjoy the merits of the two sides.
no code implementations • 2 Oct 2020 • Kun Yuan, Quanquan Li, Dapeng Chen, Aojun Zhou, Junjie Yan
To facilitate the training, we represent the network connectivity of each sample in an adjacency matrix.
1 code implementation • NeurIPS 2020 • Keyu Tian, Chen Lin, Ming Sun, Luping Zhou, Junjie Yan, Wanli Ouyang
On CIFAR-10, we achieve a top-1 error rate of 1. 24%, which is currently the best performing single model without extra training data.
no code implementations • 28 Sep 2020 • Mingzhu Shen, Feng Liang, Chuming Li, Chen Lin, Ming Sun, Junjie Yan, Wanli Ouyang
Automatic search of Quantized Neural Networks (QNN) has attracted a lot of attention.
no code implementations • ECCV 2020 • Xin Lu, Quanquan Li, Buyu Li, Junjie Yan
In this paper, we propose MimicDet, a novel and efficient framework to train a one-stage detector by directly mimic the two-stage features, aiming to bridge the accuracy gap between one-stage and two-stage detectors.
1 code implementation • 15 Sep 2020 • Haisheng Su, Weihao Gan, Wei Wu, Yu Qiao, Junjie Yan
In this paper, we present BSN++, a new framework which exploits complementary boundary regressor and relation modeling for temporal proposal generation.
no code implementations • 15 Sep 2020 • Haisheng Su, Jing Su, Dongliang Wang, Weihao Gan, Wei Wu, Mengmeng Wang, Junjie Yan, Yu Qiao
Second, the parameter frequency distribution is further adopted to guide the student network to learn the appearance modeling process from the teacher.
no code implementations • ECCV 2020 • Kun Yuan, Quanquan Li, Jing Shao, Junjie Yan
In this paper, we attempt to optimize the connectivity in neural networks.
1 code implementation • ECCV 2020 • Yuanhan Zhang, Zhenfei Yin, Yidong Li, Guojun Yin, Junjie Yan, Jing Shao, Ziwei Liu
The main reason is that current face anti-spoofing datasets are limited in both quantity and diversity.
no code implementations • 20 Jul 2020 • Haisheng Su, Jinyuan Feng, Hao Shao, Zhenyu Jiang, Manyuan Zhang, Wei Wu, Yu Liu, Hongsheng Li, Junjie Yan
Specifically, in order to generate high-quality proposals, we consider several factors including the video feature encoder, the proposal generator, the proposal-proposal relations, the scale imbalance, and ensemble strategy.
no code implementations • ECCV 2020 • Hanzhe Hu, Deyi Ji, Weihao Gan, Shuai Bai, Wei Wu, Junjie Yan
Specifically, the CDGC module takes the coarse segmentation result as class mask to extract node features for graph construction and performs dynamic graph convolutions on the constructed graph to learn the feature aggregation and weight allocation.
no code implementations • ECCV 2020 • Ronghao Guo, Chen Lin, Chuming Li, Keyu Tian, Ming Sun, Lu Sheng, Junjie Yan
Specifically, the difficulties for architecture searching in such a complex space has been eliminated by the proposed stabilized share-parameter proxy, which employs Stochastic Gradient Langevin Dynamics to enable fast shared parameter sampling, so as to achieve stabilized measurement of architecture performance even in search space with complex topological structures.
no code implementations • CVPR 2020 • Junran Peng, Xingyuan Bu, Ming Sun, Zhao-Xiang Zhang, Tieniu Tan, Junjie Yan
Training with more data has always been the most stable and effective way of improving performance in deep learning era.
no code implementations • CVPR 2020 • Shijie Yu, Shihua Li, Dapeng Chen, Rui Zhao, Junjie Yan, Yu Qiao
To address the clothes changing person re-id problem, we construct a novel large-scale re-id benchmark named ClOthes ChAnging Person Set (COCAS), which provides multiple images of the same identity with different clothes.
1 code implementation • CVPR 2020 • Shaopeng Guo, Yujie Wang, Quanquan Li, Junjie Yan
In DMCP, we model the channel pruning as a Markov process, in which each state represents for retaining the corresponding channel during pruning, and transitions between states denote the pruning process.
2 code implementations • 17 Mar 2020 • Yu Liu, Guanglu Song, Yuhang Zang, Yan Gao, Enze Xie, Junjie Yan, Chen Change Loy, Xiaogang Wang
Given such good instance bounding box, we further design a simple instance-level semantic segmentation pipeline and achieve the 1st place on the segmentation challenge.
1 code implementation • 12 Mar 2020 • Manyuan Zhang, Hao Shao, Guanglu Song, Yu Liu, Junjie Yan
In this technical report, we briefly introduce the solutions of our team 'Efficient' for the Multi-Moments in Time challenge in ICCV 2019.
1 code implementation • CVPR 2020 • Jingru Tan, Changbao Wang, Buyu Li, Quanquan Li, Wanli Ouyang, Changqing Yin, Junjie Yan
Based on it, we propose a simple but effective loss, named equalization loss, to tackle the problem of long-tailed rare categories by simply ignoring those gradients for rare categories.
Ranked #17 on Long-tail Learning on CIFAR-10-LT (ρ=10)
1 code implementation • ICLR 2020 • Junjie Yan, Ruosi Wan, Xiangyu Zhang, Wei zhang, Yichen Wei, Jian Sun
Therefore many modified normalization techniques have been proposed, which either fail to restore the performance of BN completely, or have to introduce additional nonlinear operations in inference procedure and increase huge consumption.
1 code implementation • 14 Jan 2020 • Yongqiang Yao, Yan Wang, Yu Guo, Jiaojiao Lin, Hongwei Qin, Junjie Yan
Given two or more already labeled datasets that target for different object classes, cross-dataset training aims to detect the union of the different classes, so that we do not have to label all the classes for all the datasets.
no code implementations • CVPR 2020 • Feng Zhu, Ruihao Gong, Fengwei Yu, Xianglong Liu, Yanfei Wang, Zhelong Li, Xiuqi Yang, Junjie Yan
In this paper, we give an attempt to build a unified 8-bit (INT8) training framework for common convolutional neural networks from the aspects of both accuracy and speed.
no code implementations • ICLR 2020 • Feng Liang, Chen Lin, Ronghao Guo, Ming Sun, Wei Wu, Junjie Yan, Wanli Ouyang
However, classification allocation pattern is usually adopted directly to object detector, which is proved to be sub-optimal.
no code implementations • NeurIPS 2019 • Junran Peng, Ming Sun, Zhao-Xiang Zhang, Tieniu Tan, Junjie Yan
Instead of searching and constructing an entire network, NATS explores the architecture space on the base of existing network and reusing its weights.
no code implementations • 12 Nov 2019 • Jingru Tan, Changbao Wang, Quanquan Li, Junjie Yan
Recent object detection and instance segmentation tasks mainly focus on datasets with a relatively small set of categories, e. g. Pascal VOC with 20 classes and COCO with 80 classes.
no code implementations • CVPR 2020 • Xiang Li, Chen Lin, Chuming Li, Ming Sun, Wei Wu, Junjie Yan, Wanli Ouyang
In this paper, we analyse existing weight sharing one-shot NAS approaches from a Bayesian point of view and identify the posterior fading problem, which compromises the effectiveness of shared weights.
no code implementations • 25 Sep 2019 • Kun Yuan, Quanquan Li, Yucong Zhou, Jing Shao, Junjie Yan
Seeking effective networks has become one of the most crucial and practical areas in deep learning.
1 code implementation • ICCV 2019 • Zihao Wang, Xihui Liu, Hongsheng Li, Lu Sheng, Junjie Yan, Xiaogang Wang, Jing Shao
Text-image cross-modal retrieval is a challenging task in the field of language and vision.
Ranked #9 on Image Retrieval on Flickr30K 1K test
no code implementations • 5 Sep 2019 • Junran Peng, Ming Sun, Zhao-Xiang Zhang, Tieniu Tan, Junjie Yan
With the combination of these two designs, an architecture transformation scheme could be discovered to adapt a network designed for image classification to task of object detection.
no code implementations • ICCV 2019 • Junran Peng, Ming Sun, Zhao-Xiang Zhang, Tieniu Tan, Junjie Yan
Scale-sensitive object detection remains a challenging task, where most of the existing methods could not learn it explicitly and are not robust to scale variance.
1 code implementation • 2 Sep 2019 • Yu Liu, Guanglu Song, Manyuan Zhang, Jihao Liu, Yucong Zhou, Junjie Yan
Large scale face recognition is challenging especially when the computational budget is limited.
2 code implementations • ICCV 2019 • Ruihao Gong, Xianglong Liu, Shenghu Jiang, Tianxiang Li, Peng Hu, Jiazhen Lin, Fengwei Yu, Junjie Yan
Hardware-friendly network quantization (e. g., binary/uniform quantization) can efficiently accelerate the inference and meanwhile reduce memory consumption of the deep neural networks, which is crucial for model deployment on resource-limited devices like mobile phones.
no code implementations • ICCV 2019 • Boyuan Jiang, Mengmeng Wang, Weihao Gan, Wei Wu, Junjie Yan
Spatiotemporal and motion features are two complementary and crucial information for video action recognition.
Ranked #1 on Action Recognition In Videos on HMDB-51
2 code implementations • 13 Jun 2019 • Xin Lu, Buyu Li, Yuxin Yue, Quanquan Li, Junjie Yan
Grid R-CNN is a well-performed objection detection framework.
no code implementations • 27 May 2019 • Zhenmao Li, Yichao Wu, Ken Chen, Yudong Wu, Shunfeng Zhou, Jiaheng Liu, Junjie Yan
Example weighting algorithm is an effective solution to the training bias problem, however, most previous typical methods are usually limited to human knowledge and require laborious tuning of hyperparameters.
1 code implementation • ICCV 2019 • Chen Lin, Minghao Guo, Chuming Li, Yuan Xin, Wei Wu, Dahua Lin, Wanli Ouyang, Junjie Yan
Data augmentation is critical to the success of modern deep learning techniques.
1 code implementation • ICCV 2019 • Chuming Li, Yuan Xin, Chen Lin, Minghao Guo, Wei Wu, Wanli Ouyang, Junjie Yan
The key contribution of this work is the design of search space which can guarantee the generalization and transferability on different vision tasks by including a bunch of existing prevailing loss functions in a unified formulation.
no code implementations • CVPR 2019 • Xiao Zhang, Rui Zhao, Junjie Yan, Mengya Gao, Yu Qiao, Xiaogang Wang, Hongsheng Li
Cosine-based softmax losses significantly improve the performance of deep face recognition networks.
no code implementations • ICLR 2019 • Wei Gao, Yi Wei, Quanquan Li, Hongwei Qin, Wanli Ouyang, Junjie Yan
Hints can improve the performance of student model by transferring knowledge from teacher model.
3 code implementations • CVPR 2019 • Lei Yang, Xiaohang Zhan, Dapeng Chen, Junjie Yan, Chen Change Loy, Dahua Lin
Face recognition sees remarkable progress in recent years, and its performance has reached a very high level.
2 code implementations • CVPR 2019 • Junting Pan, Chengyu Wang, Xu Jia, Jing Shao, Lu Sheng, Junjie Yan, Xiaogang Wang
This paper proposes the novel task of video generation conditioned on a SINGLE semantic label map, which provides a good balance between flexibility and quality in the generation process.
no code implementations • 28 Feb 2019 • Yingcheng Su, Shunfeng Zhou, Yi-Chao Wu, Tian Su, Ding Liang, Jiaheng Liu, Dixin Zheng, Yingxu Wang, Junjie Yan, Xiaolin Hu
Although deeper and larger neural networks have achieved better performance, the complex network structure and increasing computational cost cannot meet the demands of many resource-constrained applications.
no code implementations • 19 Feb 2019 • Chen Change Loy, Dahua Lin, Wanli Ouyang, Yuanjun Xiong, Shuo Yang, Qingqiu Huang, Dongzhan Zhou, Wei Xia, Quanquan Li, Ping Luo, Junjie Yan, Jian-Feng Wang, Zuoxin Li, Ye Yuan, Boxun Li, Shuai Shao, Gang Yu, Fangyun Wei, Xiang Ming, Dong Chen, Shifeng Zhang, Cheng Chi, Zhen Lei, Stan Z. Li, Hongkai Zhang, Bingpeng Ma, Hong Chang, Shiguang Shan, Xilin Chen, Wu Liu, Boyan Zhou, Huaxiong Li, Peng Cheng, Tao Mei, Artem Kukharenko, Artem Vasenin, Nikolay Sergievskiy, Hua Yang, Liangqi Li, Qiling Xu, Yuan Hong, Lin Chen, Mingjun Sun, Yirong Mao, Shiying Luo, Yongjun Li, Ruiping Wang, Qiaokang Xie, Ziyang Wu, Lei Lu, Yiheng Liu, Wengang Zhou
This paper presents a review of the 2018 WIDER Challenge on Face and Pedestrian.
no code implementations • ICCV 2019 • Yiru Wang, Weihao Gan, Jie Yang, Wei Wu, Junjie Yan
Human attribute analysis is a challenging task in the field of computer vision, since the data is largely imbalance-distributed.
no code implementations • 18 Jan 2019 • Weitao Feng, Zhihao Hu, Wei Wu, Junjie Yan, Wanli Ouyang
In this paper, we propose a unified Multi-Object Tracking (MOT) framework learning to make full use of long term and short term cues for handling complex cases in MOT scenes.
13 code implementations • CVPR 2019 • Bo Li, Wei Wu, Qiang Wang, Fangyi Zhang, Junliang Xing, Junjie Yan
Moreover, we propose a new model architecture to perform depth-wise and layer-wise aggregations, which not only further improves the accuracy but also reduces the model size.
Ranked #5 on Visual Object Tracking on VOT2017/18
1 code implementation • CVPR 2019 • Minghao Guo, Zhao Zhong, Wei Wu, Dahua Lin, Junjie Yan
Motivated by the fact that human-designed networks are elegant in topology with a fast inference speed, we propose a mirror stimuli function inspired by biological cognition theory to extract the abstract topological knowledge of an expert human-design network (ResNeXt).
1 code implementation • 5 Dec 2018 • Mengya Gao, Yujun Shen, Quanquan Li, Junjie Yan, Liang Wan, Dahua Lin, Chen Change Loy, Xiaoou Tang
Knowledge Distillation (KD) aims at improving the performance of a low-capacity student model by inheriting knowledge from a high-capacity teacher model.
2 code implementations • CVPR 2019 • Xin Lu, Buyu Li, Yuxin Yue, Quanquan Li, Junjie Yan
This paper proposes a novel object detection framework named Grid R-CNN, which adopts a grid guided localization mechanism for accurate object detection.
Ranked #9 on 2D Object Detection on SARDet-100K
no code implementations • NeurIPS 2018 • Chen Lin, Zhao Zhong, Wei Wu, Junjie Yan
Inspired by the relevant concept in neural science literature, we propose Synaptic Pruning: a data-driven method to prune connections between input and output feature maps with a newly proposed class of parameters called Synaptic Strength.
1 code implementation • 16 Sep 2018 • Yongcheng Liu, Lu Sheng, Jing Shao, Junjie Yan, Shiming Xiang, Chunhong Pan
Specifically, given the image-level annotations, (1) we first develop a weakly-supervised detection (WSD) model, and then (2) construct an end-to-end multi-label image classification framework augmented by a knowledge distillation module that guides the classification model by the WSD model according to the class-level predictions for the whole image and the object-level visual features for object RoIs.
Ranked #9 on Multi-Label Classification on NUS-WIDE
6 code implementations • ECCV 2018 • Xiaohang Zhan, Ziwei Liu, Junjie Yan, Dahua Lin, Chen Change Loy
Face recognition has witnessed great progress in recent years, mainly attributed to the high-capacity model designed and the abundant labeled data collected.
no code implementations • ECCV 2018 • Shiyao Wang, Yucong Zhou, Junjie Yan, Zhidong Deng
Video objection detection is challenging in the presence of appearance deterioration in certain video frames.
no code implementations • 28 Aug 2018 • Pengze Liu, Xihui Liu, Junjie Yan, Jing Shao
Pedestrian attribute recognition has attracted many attentions due to its wide applications in scene understanding and person analysis from surveillance videos.
1 code implementation • ECCV 2018 • Zheng Zhu, Qiang Wang, Bo Li, Wei Wu, Junjie Yan, Weiming Hu
During the off-line training phase, an effective sampling strategy is introduced to control this distribution and make the model focus on the semantic distractors.
Ranked #11 on Visual Object Tracking on VOT2017/18
2 code implementations • 16 Aug 2018 • Zhao Zhong, Zichen Yang, Boyang Deng, Junjie Yan, Wei Wu, Jing Shao, Cheng-Lin Liu
The block-wise generation brings unique advantages: (1) it yields state-of-the-art results in comparison to the hand-crafted networks on image classification, particularly, the best network generated by BlockQNN achieves 2. 35% top-1 error rate on CIFAR-10.
no code implementations • CVPR 2018 • Maoqing Tian, Shuai Yi, Hongsheng Li, Shihua Li, Xuesen Zhang, Jianping Shi, Junjie Yan, Xiaogang Wang
State-of-the-art methods mainly utilize deep learning based approaches for learning visual features for describing person appearances.
5 code implementations • CVPR 2018 • Bo Li, Junjie Yan, Wei Wu, Zheng Zhu, Xiaolin Hu
Visual object tracking has been a fundamental topic in recent years and many deep learning based trackers have achieved state-of-the-art performance on multiple benchmarks.
Ranked #7 on Visual Object Tracking on VOT2017/18
no code implementations • CVPR 2018 • Yujun Shen, Ping Luo, Junjie Yan, Xiaogang Wang, Xiaoou Tang
Existing methods typically formulate GAN as a two-player game, where a discriminator distinguishes face images from the real and synthesized domains, while a generator reduces its discriminativeness by synthesizing a face of photo-realistic quality.
no code implementations • ECCV 2018 • Yi Wei, Xinyu Pan, Hongwei Qin, Wanli Ouyang, Junjie Yan
To the best of our knowledge, our method, called Quantization Mimic, is the first one focusing on very tiny networks.
no code implementations • CVPR 2018 • Guanglu Song, Yu Liu, Ming Jiang, Yujie Wang, Junjie Yan, Biao Leng
Fully convolutional neural network (FCN) has been dominating the game of face detection task for a few years with its congenital capability of sliding-window-searching with shared kernels, which boiled down all the redundant calculation, and most recent state-of-the-art methods such as Faster-RCNN, SSD, YOLO and FPN use FCN as their backbone.
no code implementations • CVPR 2018 • Yu Liu, Fangyin Wei, Jing Shao, Lu Sheng, Junjie Yan, Xiaogang Wang
This paper proposes learning disentangled but complementary face features with minimal supervision by face identification.
1 code implementation • CVPR 2018 • Ruijia Xu, Ziliang Chen, WangMeng Zuo, Junjie Yan, Liang Lin
Motivated by the theoretical results in \cite{mansour2009domain}, the target distribution can be represented as the weighted combination of source distributions, and, the multi-source unsupervised domain adaptation via DCTN is then performed as two alternating steps: i) It deploys multi-way adversarial learning to minimize the discrepancy between the target and each of the multiple source domains, which also obtains the source-specific perplexity scores to denote the possibilities that a target sample belongs to different source domains.
Multi-Source Unsupervised Domain Adaptation Unsupervised Domain Adaptation
no code implementations • 5 Jan 2018 • Xingcheng Zhang, Lei Yang, Junjie Yan, Dahua Lin
Massive classification, a classification task defined over a vast number of classes (hundreds of thousands or even millions), has become an essential part of many real-world systems, such as face recognition.
7 code implementations • CVPR 2018 • Xuebo Liu, Ding Liang, Shi Yan, Dagui Chen, Yu Qiao, Junjie Yan
Incidental scene text spotting is considered one of the most difficult and valuable challenges in the document analysis community.
Ranked #4 on Scene Text Detection on ICDAR 2017 MLT
no code implementations • 16 Dec 2017 • Congrui Hetang, Hongwei Qin, Shaohui Liu, Junjie Yan
Video object detection is more challenging compared to image object detection.
1 code implementation • 9 Dec 2017 • Boyang Deng, Junjie Yan, Dahua Lin
The quest for performant networks has been a significant force that drives the advancements of deep learning in recent years.
no code implementations • CVPR 2018 • Zheng Zhu, Wei Wu, Wei Zou, Junjie Yan
Discriminative correlation filters (DCF) with deep convolutional features have achieved favorable performance in recent tracking benchmarks.
no code implementations • ICCV 2017 • Zhongdao Wang, Luming Tang, Xihui Liu, Zhuliang Yao, Shuai Yi, Jing Shao, Junjie Yan, Shengjin Wang, Hongsheng Li, Xiaogang Wang
In our vehicle ReID framework, an orientation invariant feature embedding module and a spatial-temporal regularization module are proposed.
2 code implementations • ICCV 2017 • Xihui Liu, Haiyu Zhao, Maoqing Tian, Lu Sheng, Jing Shao, Shuai Yi, Junjie Yan, Xiaogang Wang
Pedestrian analysis plays a vital role in intelligent video surveillance and is a key component for security-centric computer vision systems.
Ranked #2 on Pedestrian Attribute Recognition on RAP
1 code implementation • CVPR 2018 • Zhao Zhong, Junjie Yan, Wei Wu, Jing Shao, Cheng-Lin Liu
Convolutional neural networks have gained a remarkable success in computer vision.
1 code implementation • ICCV 2017 • Yu Liu, Hongyang Li, Junjie Yan, Fangyin Wei, Xiaogang Wang, Xiaoou Tang
To further increase efficiency and accuracy, we (a): design a scale-forecast network to globally predict potential scales in the image since there is no need to compute maps on all levels of the pyramid.
Ranked #3 on Face Detection on Annotated Faces in the Wild
no code implementations • CVPR 2017 • Quanquan Li, Shengying Jin, Junjie Yan
More specifically, we conduct a mimic method for the features sampled from the entire feature map and use a transform layer to map features from the small network onto the same dimension of the large network.
1 code implementation • CVPR 2017 • Haiyu Zhao, Maoqing Tian, Shuyang Sun, Jing Shao, Junjie Yan, Shuai Yi, Xiaogang Wang, Xiaoou Tang
Person re-identification (ReID) is an important task in video surveillance and has various applications.
no code implementations • CVPR 2017 • Zekun Hao, Yu Liu, Hongwei Qin, Junjie Yan, Xiu Li, Xiaolin Hu
Then the scale histogram guides the zoom-in and zoom-out of the image.
1 code implementation • CVPR 2017 • Yu Liu, Junjie Yan, Wanli Ouyang
In this paper, the quality aware network (QAN) is proposed to confront this problem, where the quality of each sample can be automatically learned although such information is not explicitly provided in the training stage.
Ranked #6 on Face Verification on YouTube Faces DB
1 code implementation • CVPR 2017 • Kai Kang, Hongsheng Li, Tong Xiao, Wanli Ouyang, Junjie Yan, Xihui Liu, Xiaogang Wang
Object detection in videos has drawn increasing attention recently with the introduction of the large-scale ImageNet VID dataset.
no code implementations • 19 Oct 2016 • Fengwei Yu, Wenbo Li, Quanquan Li, Yu Liu, Xiaohua Shi, Junjie Yan
In this paper, we explore the high-performance detection and deep learning based appearance feature, and show that they lead to significantly better MOT results in both online and offline setting.
1 code implementation • 8 Oct 2016 • Xingyu Zeng, Wanli Ouyang, Junjie Yan, Hongsheng Li, Tong Xiao, Kun Wang, Yu Liu, Yucong Zhou, Bin Yang, Zhe Wang, Hui Zhou, Xiaogang Wang
The effectiveness of GBD-Net is shown through experiments on three object detection datasets, ImageNet, Pascal VOC2007 and Microsoft COCO.
no code implementations • CVPR 2016 • Hongwei Qin, Junjie Yan, Xiu Li, Xiaolin Hu
Cascade has been widely used in face detection, where classifier with low computation cost can be firstly used to shrink most of the background while keeping the recall.
1 code implementation • CVPR 2016 • Bin Yang, Junjie Yan, Zhen Lei, Stan Z. Li
They decompose the object detection problem into two cascaded easier tasks: 1) generating object proposals from images, 2) classifying proposals into various object categories.
1 code implementation • 9 Apr 2016 • Kai Kang, Hongsheng Li, Junjie Yan, Xingyu Zeng, Bin Yang, Tong Xiao, Cong Zhang, Zhe Wang, Ruohui Wang, Xiaogang Wang, Wanli Ouyang
Temporal and contextual information of videos are not fully investigated and utilized.
no code implementations • CVPR 2015 • Junjie Yan, Yinan Yu, Xiangyu Zhu, Zhen Lei, Stan Z. Li
Object detection is always conducted by object proposal generation and classification sequentially.
no code implementations • CVPR 2015 • Xiangyu Zhu, Zhen Lei, Junjie Yan, Dong Yi, Stan Z. Li
Pose and expression normalization is a crucial step to recover the canonical view of faces under arbitrary conditions, so as to improve the face recognition performance.
1 code implementation • ICCV 2015 • Bin Yang, Junjie Yan, Zhen Lei, Stan Z. Li
With the combination of CNN features and boosting forest, CCF benefits from the richer capacity in feature representation compared with channel features, as well as lower cost in computation and storage compared with end-to-end CNN methods.
no code implementations • 16 Apr 2015 • Tamara Bonaci, Jeffrey Herron, Tariq Yusuf, Junjie Yan, Tadayoshi Kohno, Howard Jay Chizeck
Our work seeks to answer this question by systematically analyzing possible cyber security attacks against Raven II, an advanced teleoperated robotic surgery system.
Robotics Cryptography and Security
no code implementations • 15 Jul 2014 • Bin Yang, Junjie Yan, Zhen Lei, Stan Z. Li
Face detection has drawn much attention in recent decades since the seminal work by Viola and Jones.
Ranked #37 on Face Detection on WIDER Face (Medium)
no code implementations • CVPR 2014 • Junjie Yan, Zhen Lei, Longyin Wen, Stan Z. Li
Three prohibitive steps in cascade version of DPM are accelerated, including 2D correlation between root filter and feature map, cascade part pruning and HOG feature extraction.
no code implementations • CVPR 2014 • Longyin Wen, Wenbo Li, Junjie Yan, Zhen Lei, Dong Yi, Stan Z. Li
Multi-target tracking is an interesting but challenging task in computer vision field.
no code implementations • CVPR 2013 • Junjie Yan, Xucong Zhang, Zhen Lei, Shengcai Liao, Stan Z. Li
The model contains resolution aware transformations to map pedestrians in different resolutions to a common space, where a shared detector is constructed to distinguish pedestrians from background.