no code implementations • 30 Mar 2023 • Chongjian Ge, Jiangliu Wang, Zhan Tong, Shoufa Chen, Yibing Song, Ping Luo
We evaluate our soft neighbor contrastive learning method (SNCLR) on standard visual recognition benchmarks, including image classification, object detection, and instance segmentation.
no code implementations • 30 Mar 2023 • Yuanfeng Ji, Zhe Chen, Enze Xie, Lanqing Hong, Xihui Liu, Zhaoqiang Liu, Tong Lu, Zhenguo Li, Ping Luo
We propose a simple, efficient, yet powerful framework for dense visual predictions based on the conditional diffusion pipeline.
no code implementations • 29 Mar 2023 • Zhaoyang Zhang, Yitong Jiang, Wenqi Shao, Xiaogang Wang, Ping Luo, Kaimo Lin, Jinwei Gu
Controllable image denoising aims to generate clean samples with human perceptual priors and balance sharpness and smoothness.
1 code implementation • 24 Mar 2023 • Teng Wang, Yixiao Ge, Feng Zheng, Ran Cheng, Ying Shan, XiaoHu Qie, Ping Luo
FLM successfully frees the prediction rate from the tie-up with the corruption rate while allowing the corruption spans to be customized for each token to be predicted.
1 code implementation • 22 Mar 2023 • Shilong Zhang, Wang xinjiang, Jiaqi Wang, Jiangmiao Pang, Chengqi Lyu, Wenwei Zhang, Ping Luo, Kai Chen
Concretely, we first lay dense queries like traditional detectors and then select distinct ones for one-to-one assignments.
1 code implementation • 19 Mar 2023 • Haibao Yu, Yingjuan Tang, Enze Xie, Jilei Mao, Jirui Yuan, Ping Luo, Zaiqing Nie
Cooperatively utilizing both ego-vehicle and infrastructure sensor data can significantly enhance autonomous driving perception abilities.
1 code implementation • 12 Mar 2023 • Bin Yan, Yi Jiang, Jiannan Wu, Dong Wang, Ping Luo, Zehuan Yuan, Huchuan Lu
All instance perception tasks aim at finding certain objects specified by some queries such as category names, language expressions, and target annotations, but this complete field has been split into multiple independent subtasks.
Ranked #1 on
Referring Expression Comprehension
on RefCOCOg-test
(using extra training data)
Multi-Object Tracking and Segmentation
Multiple Object Tracking
+12
no code implementations • 11 Mar 2023 • Teng Wang, Jinrui Zhang, Feng Zheng, Wenhao Jiang, Ran Cheng, Ping Luo
TEG learns to adaptively ground the possible event proposals given a set of sentences by estimating the cross-modal distance in a joint semantic space.
no code implementations • 3 Feb 2023 • Zhixuan Liang, Yao Mu, Mingyu Ding, Fei Ni, Masayoshi Tomizuka, Ping Luo
For example, AdaptDiffuser not only outperforms the previous art Diffuser by 20. 8% on Maze2D and 7. 5% on MuJoCo locomotion, but also adapts better to new tasks, e. g., KUKA pick-and-place, by 27. 9% without requiring additional expert data.
no code implementations • 27 Jan 2023 • Jie Zhu, Jiyang Qi, Mingyu Ding, Xiaokang Chen, Ping Luo, Xinggang Wang, Wenyu Liu, Leye Wang, Jingdong Wang
The study is mainly motivated by that random views, used in contrastive learning, and random masked (visible) patches, used in masked image modeling, are often about object parts.
1 code implementation • 19 Jan 2023 • Bin Huang, Yangguang Li, Enze Xie, Feng Liang, Luya Wang, Mingzhu Shen, Fenggang Liu, Tianqi Wang, Ping Luo, Jing Shao
Recently, the pure camera-based Bird's-Eye-View (BEV) perception removes expensive Lidar sensors, making it a feasible solution for economical autonomous driving.
no code implementations • 14 Dec 2022 • Yuying Ge, Annabella Macaluso, Li Erran Li, Ping Luo, Xiaolong Wang
When deploying the trained policy to a new task or a new environment, we first let the policy play with randomly generated instructions to record the demonstrations.
1 code implementation • 27 Nov 2022 • Chuang Lin, Peize Sun, Yi Jiang, Ping Luo, Lizhen Qu, Gholamreza Haffari, Zehuan Yuan, Jianfei Cai
In this paper, we propose a novel open-vocabulary object detection framework directly learning from image-text pair data.
no code implementations • 24 Nov 2022 • Yao Lai, Yao Mu, Ping Luo
Firstly, MaskPlace recasts placement as a problem of learning pixel-level visual representation to comprehensively describe millions of modules on a chip, enabling placement in a high-resolution canvas and a large action space.
no code implementations • 23 Nov 2022 • Junjie Wang, Yao Mu, Dong Li, Qichao Zhang, Dongbin Zhao, Yuzheng Zhuang, Ping Luo, Bin Wang, Jianye Hao
The latent world model provides a promising way to learn policies in a compact latent space for tasks with high-dimensional observations, however, its generalization across diverse environments with unseen dynamics remains challenging.
Model-based Reinforcement Learning
reinforcement-learning
+1
3 code implementations • 17 Nov 2022 • Shoufa Chen, Peize Sun, Yibing Song, Ping Luo
In inference, the model refines a set of randomly generated boxes to the output results in a progressive way.
1 code implementation • 20 Oct 2022 • Zeyue Xue, Jianming Liang, Guanglu Song, Zhuofan Zong, Liang Chen, Yu Liu, Ping Luo
To address this challenge, we propose a simple yet effective algorithm, named Adaptive Gradient Variance Modulator (AGVM), which can train dense visual predictors with very large batch size, enabling several benefits more appealing than prior arts.
1 code implementation • 9 Oct 2022 • Yao Mu, Yuzheng Zhuang, Fei Ni, Bin Wang, Jianyu Chen, Jianye Hao, Ping Luo
This paper addresses such a challenge by Decomposed Mutual INformation Optimization (DOMINO) for context learning, which explicitly learns a disentangled context to maximize the mutual information between the context and historical trajectories, while minimizing the state transition prediction error.
no code implementations • 8 Oct 2022 • Zeyu Gao, Yao Mu, Ruoyan Shen, Chen Chen, Yangang Ren, Jianyu Chen, Shengbo Eben Li, Ping Luo, YanFeng Lu
End-to-end autonomous driving provides a feasible way to automatically maximize overall driving system performance by directly mapping the raw pixels from a front-facing camera to control signals.
1 code implementation • 30 Sep 2022 • Ziyun Zeng, Yuying Ge, Xihui Liu, Bin Chen, Ping Luo, Shu-Tao Xia, Yixiao Ge
Pre-training on large-scale video data has become a common recipe for learning transferable spatiotemporal representations in recent years.
no code implementations • 28 Sep 2022 • Ping Luo, Jieren Cheng, Zhenhao Liu, N. Xiong, Jie Wu
However, the clients' Non-Independent and Identically Distributed (Non-IID) data negatively affect the trained model, and clients with different numbers of local updates may cause significant gaps to the local gradients in each communication round.
1 code implementation • 26 Sep 2022 • Chuofan Ma, Qiushan Guo, Yi Jiang, Zehuan Yuan, Ping Luo, Xiaojuan Qi
Our key finding is that the major cause of degradation is not information loss in the down-sampling process, but rather the mismatch between network architecture and input scale.
2 code implementations • 12 Sep 2022 • Hongyang Li, Chonghao Sima, Jifeng Dai, Wenhai Wang, Lewei Lu, Huijie Wang, Enze Xie, Zhiqi Li, Hanming Deng, Hao Tian, Xizhou Zhu, Li Chen, Yulu Gao, Xiangwei Geng, Jia Zeng, Yang Li, Jiazhi Yang, Xiaosong Jia, Bohan Yu, Yu Qiao, Dahua Lin, Si Liu, Junchi Yan, Jianping Shi, Ping Luo
As sensor configurations get more complex, integrating multi-source information from different sensors and representing features in a unified view come of vital importance.
1 code implementation • 23 Aug 2022 • Lumin Xu, Sheng Jin, Wentao Liu, Chen Qian, Wanli Ouyang, Ping Luo, Xiaogang Wang
We propose a single-network approach, termed ZoomNet, to take into account the hierarchical structure of the full human body and solve the scale variation of different body parts.
Ranked #2 on
2D Human Pose Estimation
on COCO-WholeBody
1 code implementation • 16 Aug 2022 • Wentao Jiang, Sheng Jin, Wentao Liu, Chen Qian, Ping Luo, Si Liu
Human pose estimation aims to accurately estimate a wide variety of human poses.
1 code implementation • 22 Jul 2022 • Hao Meng, Sheng Jin, Wentao Liu, Chen Qian, Mengxiang Lin, Wanli Ouyang, Ping Luo
Unlike most previous works that directly predict the 3D poses of two interacting hands simultaneously, we propose to decompose the challenging interacting hand pose estimation task and estimate the pose of each hand separately.
1 code implementation • 21 Jul 2022 • Lumin Xu, Sheng Jin, Wang Zeng, Wentao Liu, Chen Qian, Wanli Ouyang, Ping Luo, Xiaogang Wang
In this paper, we introduce the task of Category-Agnostic Pose Estimation (CAPE), which aims to create a pose estimation model capable of detecting the pose of any class of object given only a few samples with keypoint definition.
no code implementations • 18 Jul 2022 • Wejia Wu, Zhuang Li, Jiahong Li, Chunhua Shen, Hong Zhou, Size Li, Zhongyuan Wang, Ping Luo
Our contributions are three-fold: 1) CoText simultaneously address the three tasks (e. g., text detection, tracking, recognition) in a real-time end-to-end trainable framework.
1 code implementation • 14 Jul 2022 • Bin Yan, Yi Jiang, Peize Sun, Dong Wang, Zehuan Yuan, Ping Luo, Huchuan Lu
We present a unified method, termed Unicorn, that can simultaneously solve four tracking problems (SOT, MOT, VOS, MOTS) with a single network using the same model parameters.
Multi-Object Tracking
Multi-Object Tracking and Segmentation
+2
1 code implementation • 7 Jul 2022 • Wenqi Shao, Xun Zhao, Yixiao Ge, Zhaoyang Zhang, Lei Yang, Xiaogang Wang, Ying Shan, Ping Luo
It is challenging because the ground-truth model ranking for each task can only be generated by fine-tuning the pre-trained models on the target dataset, which is brute-force and computationally expensive.
1 code implementation • 3 Jul 2022 • Jinrui Zhang, Teng Wang, Feng Zheng, Ran Cheng, Ping Luo
Previous methods only process the information of a single boundary at a time, which lacks utilization of video context information.
1 code implementation • 17 Jun 2022 • Teng Wang, Wenhao Jiang, Zhichao Lu, Feng Zheng, Ran Cheng, Chengguo Yin, Ping Luo
Existing vision-language pre-training (VLP) methods primarily rely on paired image-text datasets, which are either annotated by enormous human labors, or crawled from the internet followed by elaborate data cleaning techniques.
1 code implementation • 17 Jun 2022 • Yao Mu, Shoufa Chen, Mingyu Ding, Jianyu Chen, Runjian Chen, Ping Luo
In visual control, learning transferable state representation that can transfer between different control tasks is important to reduce the training sample size.
1 code implementation • 16 Jun 2022 • Yuanfeng Ji, Haotian Bai, Jie Yang, Chongjian Ge, Ye Zhu, Ruimao Zhang, Zhen Li, Lingyan Zhang, Wanling Ma, Xiang Wan, Ping Luo
Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of various methods.
1 code implementation • 8 Jun 2022 • Runjian Chen, Yao Mu, Runsen Xu, Wenqi Shao, Chenhan Jiang, Hang Xu, Zhenguo Li, Ping Luo
In this paper, we propose CO^3, namely Cooperative Contrastive Learning and Contextual Shape Prediction, to learn 3D representation for outdoor-scene point clouds in an unsupervised manner.
1 code implementation • 26 May 2022 • Shoufa Chen, Chongjian Ge, Zhan Tong, Jiangliu Wang, Yibing Song, Jue Wang, Ping Luo
To address this challenge, we propose an effective adaptation approach for Transformer, namely AdaptFormer, which can adapt the pre-trained ViTs into many different image and video tasks efficiently.
no code implementations • 23 May 2022 • Xiaoyu Chen, Yao Mu, Ping Luo, Shengbo Li, Jianyu Chen
Furthermore, we show that the learned belief states can be plugged into downstream RL algorithms to improve performance.
2 code implementations • 16 May 2022 • Xin Chen, Sam Toyer, Cody Wild, Scott Emmons, Ian Fischer, Kuang-Huei Lee, Neel Alex, Steven H Wang, Ping Luo, Stuart Russell, Pieter Abbeel, Rohin Shah
We propose a modular framework for constructing representation learning algorithms, then use our framework to evaluate the utility of representation learning for imitation across several environment suites.
1 code implementation • 26 Apr 2022 • Yuying Ge, Yixiao Ge, Xihui Liu, Alex Jinpeng Wang, Jianping Wu, Ying Shan, XiaoHu Qie, Ping Luo
Dominant pre-training work for video-text retrieval mainly adopt the "dual-encoder" architectures to enable efficient retrieval, where two separate encoders are used to contrast global video and text representations, but ignore detailed local semantics.
1 code implementation • CVPR 2022 • Wang Zeng, Sheng Jin, Wentao Liu, Chen Qian, Ping Luo, Wanli Ouyang, Xiaogang Wang
Vision transformers have achieved great successes in many computer vision tasks.
Ranked #3 on
2D Human Pose Estimation
on COCO-WholeBody
no code implementations • 13 Apr 2022 • Teng Wang, Zhu Liu, Feng Zheng, Zhichao Lu, Ran Cheng, Ping Luo
This report describes the details of our approach for the event dense-captioning task in ActivityNet Challenge 2021.
no code implementations • 11 Apr 2022 • Enze Xie, Zhiding Yu, Daquan Zhou, Jonah Philion, Anima Anandkumar, Sanja Fidler, Ping Luo, Jose M. Alvarez
In this paper, we propose M$^2$BEV, a unified framework that jointly performs 3D object detection and map segmentation in the Birds Eye View~(BEV) space with multi-camera image inputs.
3 code implementations • 7 Apr 2022 • Mingyu Ding, Bin Xiao, Noel Codella, Ping Luo, Jingdong Wang, Lu Yuan
We show that these two self-attentions complement each other: (i) since each channel token contains an abstract representation of the entire image, the channel attention naturally captures global interactions and representations by taking all spatial positions into account when computing attention scores between channels; (ii) the spatial attention refines the local representations by performing fine-grained interactions across spatial locations, which in turn helps the global information modeling in channel attention.
Ranked #11 on
Image Classification
on ImageNet
(using extra training data)
no code implementations • CVPR 2022 • Qiushan Guo, Yao Mu, Jianyu Chen, Tianqi Wang, Yizhou Yu, Ping Luo
Further, we overcome these challenges by introducing a novel approach, Scale-Equivalent Distillation (SED), which is a simple yet effective end-to-end knowledge distillation framework robust to large object size variance and class imbalance.
no code implementations • ACL 2022 • Chaofan Tao, Lu Hou, Wei zhang, Lifeng Shang, Xin Jiang, Qun Liu, Ping Luo, Ngai Wong
We find that previous quantization methods fail on generative tasks due to the \textit{homogeneous word embeddings} caused by reduced capacity, and \textit{varied distribution of weights}.
1 code implementation • 20 Mar 2022 • Weijia Wu, Yuanqiang Cai, Chunhua Shen, Debing Zhang, Ying Fu, Hong Zhou, Ping Luo
Recent video text spotting methods usually require the three-staged pipeline, i. e., detecting text in individual images, recognizing localized text, tracking text streams with post-processing to generate final results.
no code implementations • 16 Mar 2022 • Chunmeng Liu, Enze Xie, Wenjia Wang, Wenhai Wang, Guangyao Li, Ping Luo
Although convolutional neural networks (CNNs) have achieved remarkable progress in weakly supervised semantic segmentation (WSSS), the effective receptive field of CNN is insufficient to capture global context information, leading to sub-optimal results.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
no code implementations • 21 Feb 2022 • Zhecheng Yuan, Guozheng Ma, Yao Mu, Bo Xia, Bo Yuan, Xueqian Wang, Ping Luo, Huazhe Xu
One of the key challenges in visual Reinforcement Learning (RL) is to learn policies that can generalize to unseen environments.
4 code implementations • 7 Feb 2022 • Xiaokang Chen, Mingyu Ding, Xiaodi Wang, Ying Xin, Shentong Mo, Yunhao Wang, Shumin Han, Ping Luo, Gang Zeng, Jingdong Wang
We introduce an alignment constraint, encouraging that the representations for masked patches, predicted from the encoded representations of visible patches, are aligned with the masked patch presentations computed from the encoder.
1 code implementation • 24 Jan 2022 • Yuanfeng Ji, Lu Zhang, Jiaxiang Wu, Bingzhe Wu, Long-Kai Huang, Tingyang Xu, Yu Rong, Lanqing Li, Jie Ren, Ding Xue, Houtim Lai, Shaoyong Xu, Jing Feng, Wei Liu, Ping Luo, Shuigeng Zhou, Junzhou Huang, Peilin Zhao, Yatao Bian
AI-aided drug discovery (AIDD) is gaining increasing popularity due to its promise of making the search for new pharmaceuticals quicker, cheaper and more efficient.
no code implementations • ICLR 2022 • Can Wang, Sheng Jin, Yingda Guan, Wentao Liu, Chen Qian, Ping Luo, Wanli Ouyang
PL approaches apply pseudo-labels to unlabeled data, and then train the model with a combination of the labeled and pseudo-labeled data iteratively.
1 code implementation • CVPR 2022 • Zhouxia Wang, Jiawei Zhang, Runjian Chen, Wenping Wang, Ping Luo
Blind face restoration is to recover a high-quality face image from unknown degradations.
no code implementations • 13 Jan 2022 • Yuying Ge, Yibing Song, Ruimao Zhang, Ping Luo
Dancing video retargeting aims to synthesize a video that transfers the dance movements from a source video to a target person.
2 code implementations • CVPR 2022 • Yuying Ge, Yixiao Ge, Xihui Liu, Dian Li, Ying Shan, XiaoHu Qie, Ping Luo
As an additional benefit, our method achieves competitive results with much shorter pre-training videos on single-modality downstream tasks, e. g., action recognition with linear evaluation.
Ranked #32 on
Video Retrieval
on MSR-VTT-1kA
(using extra training data)
1 code implementation • CVPR 2022 • Jiannan Wu, Yi Jiang, Peize Sun, Zehuan Yuan, Ping Luo
Referring video object segmentation (R-VOS) is an emerging cross-modal task that aims to segment the target object referred by a language expression in all video frames.
Ranked #1 on
Referring Expression Segmentation
on A2D Sentences
(using extra training data)
no code implementations • 6 Dec 2021 • Yuying Ge, Ruimao Zhang, Ping Luo
This work proposes a novel framework named MetaCloth via meta-learning, which is able to learn unseen tasks of dense fashion landmark detection with only a few annotated samples.
1 code implementation • ICLR 2022 • Wenqi Shao, Yixiao Ge, Zhaoyang Zhang, Xuyuan Xu, Xiaogang Wang, Ying Shan, Ping Luo
It is difficult for Transformers to capture inductive bias such as the positional context in an image with LN.
no code implementations • NeurIPS 2021 • Yao Mu, Yuzheng Zhuang, Bin Wang, Guangxiang Zhu, Wulong Liu, Jianyu Chen, Ping Luo, Shengbo Li, Chongjie Zhang, Jianye Hao
Model-based reinforcement learning aims to improve the sample efficiency of policy learning by modeling the dynamics of the environment.
Model-based Reinforcement Learning
reinforcement-learning
+1
no code implementations • NeurIPS 2021 • Yuqi Huo, Mingyu Ding, Haoyu Lu, Nanyi Fei, Zhiwu Lu, Ji-Rong Wen, Ping Luo
To enhance the representation ability of the motion vectors, hence the effectiveness of our method, we design a cross guidance contrastive learning algorithm based on multi-instance InfoNCE loss, where motion vectors can take supervision signals from RGB frames and vice versa.
1 code implementation • NeurIPS 2021 • Chongjian Ge, Youwei Liang, Yibing Song, Jianbo Jiao, Jue Wang, Ping Luo
Motivated by the transformers that explore visual attention effectively in recognition scenarios, we propose a CNN Attention REvitalization (CARE) framework to train attentive CNN encoders guided by transformers in SSL.
3 code implementations • CVPR 2022 • Peize Sun, Jinkun Cao, Yi Jiang, Zehuan Yuan, Song Bai, Kris Kitani, Ping Luo
A typical pipeline for multi-object tracking (MOT) is to use a detector for object localization, and following re-identification (re-ID) for object association.
2 code implementations • 3 Nov 2021 • Zhe Chen, Jiahao Wang, Wenhai Wang, Guo Chen, Enze Xie, Ping Luo, Tong Lu
We propose an accurate and efficient scene text detection framework, termed FAST (i. e., faster arbitrarily-shaped text detector).
Ranked #1 on
Scene Text Detection
on MSRA-TD500
no code implementations • NeurIPS 2021 • Mingyu Ding, Zhenfang Chen, Tao Du, Ping Luo, Joshua B. Tenenbaum, Chuang Gan
This is achieved by seamlessly integrating three components: a visual perception module, a concept learner, and a differentiable physics engine.
9 code implementations • arXiv 2021 • Yifu Zhang, Peize Sun, Yi Jiang, Dongdong Yu, Fucheng Weng, Zehuan Yuan, Ping Luo, Wenyu Liu, Xinggang Wang
ByteTrack also achieves state-of-the-art performance on MOT20, HiEve and BDD100K tracking benchmarks.
Ranked #1 on
Multiple Object Tracking
on BDD100K val
1 code implementation • 11 Oct 2021 • Chongjian Ge, Youwei Liang, Yibing Song, Jianbo Jiao, Jue Wang, Ping Luo
Motivated by the transformers that explore visual attention effectively in recognition scenarios, we propose a CNN Attention REvitalization (CARE) framework to train attentive CNN encoders guided by transformers in SSL.
no code implementations • ICLR 2022 • Shuo Yang, Peize Sun, Yi Jiang, Xiaobo Xia, Ruiheng Zhang, Zehuan Yuan, Changhu Wang, Ping Luo, Min Xu
A more realistic object detection paradigm, Open-World Object Detection, has arisen increasing research interests in the community recently.
no code implementations • 29 Sep 2021 • Qiushan Guo, Yizhou Yu, Ping Luo
Furthermore, the limited annotations in semi-supervised learning scale up the challenges: large variance of object sizes and class imbalance (i. e., the extreme ratio between background and object), hindering the performance of prior arts.
1 code implementation • 18 Sep 2021 • Jiannan Wu, Peize Sun, Shoufa Chen, Jiewen Yang, Zihao Qi, Lan Ma, Ping Luo
Towards high-quality temporal action detection, we introduce Sparse Proposals to interact with the hierarchical features.
1 code implementation • CVPR 2022 • Zhiqi Li, Wenhai Wang, Enze Xie, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo, Tong Lu
Specifically, we supervise the attention modules in the mask decoder in a layer-wise manner.
Ranked #4 on
Panoptic Segmentation
on COCO test-dev
no code implementations • ICCV 2021 • Muhammad Awais, Fengwei Zhou, Hang Xu, Lanqing Hong, Ping Luo, Sung-Ho Bae, Zhenguo Li
Extensive Unsupervised Domain Adaptation (UDA) studies have shown great success in practice by learning transferable representations across a labeled source domain and an unlabeled target domain with deep models.
1 code implementation • ICCV 2021 • Teng Wang, Ruimao Zhang, Zhichao Lu, Feng Zheng, Ran Cheng, Ping Luo
Dense video captioning aims to generate multiple associated captions with their temporal locations from the video.
Ranked #2 on
Dense Video Captioning
on YouCook2
8 code implementations • ICLR 2022 • Shoufa Chen, Enze Xie, Chongjian Ge, Runjian Chen, Ding Liang, Ping Luo
We build a family of models which surpass existing MLPs and even state-of-the-art Transformer-based models, e. g., Swin Transformer, while using fewer parameters and FLOPs.
Ranked #15 on
Semantic Segmentation
on DensePASS
1 code implementation • 8 Jul 2021 • Lingyun Wu, Zhiqiang Hu, Yuanfeng Ji, Ping Luo, Shaoting Zhang
For example, STFT improves the still image baseline FCOS by 10. 6% and 20. 6% on the comprehensive F1-score of the polyp localization task in CVC-Clinic and ASUMayo datasets, respectively, and outperforms the state-of-the-art video-based method by 3. 6% and 8. 0%, respectively.
1 code implementation • 28 Jun 2021 • Yuanfeng Ji, Ruimao Zhang, Huijie Wang, Zhen Li, Lingyun Wu, Shaoting Zhang, Ping Luo
The recent vision transformer(i. e. for image classification) learns non-local attentive interaction of different patch tokens.
11 code implementations • 25 Jun 2021 • Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao
We hope this work will facilitate state-of-the-art Transformer researches in computer vision.
Ranked #60 on
Object Detection
on COCO minival
1 code implementation • CVPR 2021 • Mingyu Ding, Xiaochen Lian, Linjie Yang, Peng Wang, Xiaojie Jin, Zhiwu Lu, Ping Luo
Last, we proposed an efficient fine-grained search strategy to train HR-NAS, which effectively explores the search space, and finds optimal architectures given various tasks and computation resources.
14 code implementations • NeurIPS 2021 • Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo
We present SegFormer, a simple, efficient yet powerful semantic segmentation framework which unifies Transformers with lightweight multilayer perception (MLP) decoders.
Ranked #2 on
Semantic Segmentation
on SELMA
4 code implementations • CVPR 2021 • Lumin Xu, Yingda Guan, Sheng Jin, Wentao Liu, Chen Qian, Ping Luo, Wanli Ouyang, Xiaogang Wang
Human pose estimation has achieved significant progress in recent years.
Ranked #26 on
Pose Estimation
on COCO test-dev
(using extra training data)
no code implementations • 14 May 2021 • Rongyu Cao, Yixuan Cao, Ganbin Zhou, Ping Luo
In this paper, we study the problem of extracting variable-depth "logical document hierarchy" from long documents, namely organizing the recognized "physical document objects" into hierarchical structures.
1 code implementation • CVPR 2021 • Jiahang Wang, Sheng Jin, Wentao Liu, Weizhong Liu, Chen Qian, Ping Luo
However, unlike human vision that is robust to various data corruptions such as blur and pixelation, current pose estimators are easily confused by these corruptions.
no code implementations • 13 May 2021 • Wenqi Shao, Hang Yu, Zhaoyang Zhang, Hang Xu, Zhenguo Li, Ping Luo
To address this problem, we develop a probability-based pruning algorithm, called batch whitening channel pruning (BWCP), which can stochastically discard unimportant channels by modeling the probability of a channel being activated.
1 code implementation • 5 May 2021 • Enze Xie, Wenhai Wang, Mingyu Ding, Ruimao Zhang, Ping Luo
Extensive experiments demonstrate the effectiveness of both PolarMask and PolarMask++, which achieve competitive results on instance segmentation in the challenging COCO dataset with single-model and single-scale training and testing, as well as new state-of-the-art results on rotate text detection and cell segmentation.
Ranked #77 on
Instance Segmentation
on COCO test-dev
(using extra training data)
no code implementations • 27 Mar 2021 • Shervin Minaee, Ping Luo, Zhe Lin, Kevin Bowyer
In this work, we provide a detailed overview of some of the most representative deep learning based face detection methods by grouping them into a few major categories, and present their core architectural designs and accuracies on popular benchmarks.
1 code implementation • ICLR 2022 • Mingyu Ding, Yuqi Huo, Haoyu Lu, Linjie Yang, Zhe Wang, Zhiwu Lu, Jingdong Wang, Ping Luo
(4) Thorough studies of NCP on inter-, cross-, and intra-tasks highlight the importance of cross-task neural architecture design, i. e., multitask neural architectures and architecture transferring between different tasks.
1 code implementation • 22 Mar 2021 • Zhe Chen, Wenhai Wang, Enze Xie, Tong Lu, Ping Luo
(1) We divide input image into small patches and adopt TIN, successfully transferring image style with arbitrary high-resolution.
1 code implementation • CVPR 2021 • Chongjian Ge, Yibing Song, Yuying Ge, Han Yang, Wei Liu, Ping Luo
To this end, DCTON can be naturally trained in a self-supervised manner following cycle consistency learning.
1 code implementation • CVPR 2021 • Yuying Ge, Yibing Song, Ruimao Zhang, Chongjian Ge, Wei Liu, Ping Luo
A recent pioneering work employed knowledge distillation to reduce the dependency of human parsing, where the try-on images produced by a parser-based method are used as supervisions to train a "student" network without relying on segmentation, making the student mimic the try-on ability of the parser-based model.
Ranked #1 on
Virtual Try-on
on MPV
no code implementations • 8 Mar 2021 • Jian Ding, Enze Xie, Hang Xu, Chenhan Jiang, Zhenguo Li, Ping Luo, Gui-Song Xia
Unsupervised pre-training aims at learning transferable features that are beneficial for downstream tasks.
9 code implementations • ICCV 2021 • Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao
Unlike the recently-proposed Transformer model (e. g., ViT) that is specially designed for image classification, we propose Pyramid Vision Transformer~(PVT), which overcomes the difficulties of porting Transformer to various dense prediction tasks.
Ranked #5 on
Semantic Segmentation
on SynPASS
1 code implementation • 15 Feb 2021 • Chaofan Tao, Rui Lin, Quan Chen, Zhaoyang Zhang, Ping Luo, Ngai Wong
Prior arts often discretize the network weights by carefully tuning hyper-parameters of quantization (e. g. non-uniform stepsize and layer-wise bitwidths), which are complicated and sub-optimal because the full-precision and low-precision models have a large discrepancy.
2 code implementations • ICCV 2021 • Enze Xie, Jian Ding, Wenhai Wang, Xiaohang Zhan, Hang Xu, Peize Sun, Zhenguo Li, Ping Luo
Unlike most recent methods that focused on improving accuracy of image classification, we present a novel contrastive learning approach, named DetCo, which fully explores the contrasts between global image and local image patches to learn discriminative representations for object detection.
2 code implementations • 21 Jan 2021 • Enze Xie, Wenjia Wang, Wenhai Wang, Peize Sun, Hang Xu, Ding Liang, Ping Luo
This work presents a new fine-grained transparent object segmentation dataset, termed Trans10K-v2, extending Trans10K-v1, the first large-scale transparent object segmentation dataset.
Ranked #3 on
Semantic Segmentation
on Trans10K
no code implementations • NeurIPS 2021 • Zhongzhan Huang, Xinjiang Wang, Ping Luo
Channel pruning is a popular technique for compressing convolutional neural networks (CNNs), and various pruning criteria have been proposed to remove the redundant filters of CNNs.
1 code implementation • ICCV 2021 • Zhaoyang Zhang, Yitong Jiang, Jun Jiang, Xiaogang Wang, Ping Luo, Jinwei Gu
STAR is a general architecture that can be easily adapted to different image enhancement tasks.
no code implementations • 1 Jan 2021 • Yuqi Huo, Mingyu Ding, Haoyu Lu, Zhiwu Lu, Tao Xiang, Ji-Rong Wen, Ziyuan Huang, Jianwen Jiang, Shiwei Zhang, Mingqian Tang, Songfang Huang, Ping Luo
With the constrained jigsaw puzzles, instead of solving them directly, which could still be extremely hard, we carefully design four surrogate tasks that are more solvable but meanwhile still ensure that the learned representation is sensitive to spatiotemporal continuity at both the local and global levels.
1 code implementation • ICCV 2021 • Wei Shang, Dongwei Ren, Dongqing Zou, Jimmy S. Ren, Ping Luo, WangMeng Zuo
EFM can also be easily incorporated into existing deblurring networks, making event-driven deblurring task benefit from state-of-the-art deblurring methods.
no code implementations • ICCV 2021 • Shoufa Chen, Peize Sun, Enze Xie, Chongjian Ge, Jiannan Wu, Lan Ma, Jiajun Shen, Ping Luo
WOO takes a unified video backbone to simultaneously extract features for actor location and action classification.
3 code implementations • 31 Dec 2020 • Peize Sun, Jinkun Cao, Yi Jiang, Rufeng Zhang, Enze Xie, Zehuan Yuan, Changhu Wang, Ping Luo
In this work, we propose TransTrack, a simple but efficient scheme to solve the multiple object tracking problems.
Ranked #9 on
Multi-Object Tracking
on DanceTrack
1 code implementation • 10 Dec 2020 • Peize Sun, Yi Jiang, Enze Xie, Wenqi Shao, Zehuan Yuan, Changhu Wang, Ping Luo
We identify that classification cost in matching cost is the main ingredient: (1) previous detectors only consider location cost, (2) by additionally introducing classification cost, previous detectors immediately produce one-to-one prediction during inference.
1 code implementation • 26 Nov 2020 • Weijia Wu, Enze Xie, Ruimao Zhang, Wenhai Wang, Hong Zhou, Ping Luo
For example, without using polygon annotations, PSENet achieves an 80. 5% F-score on TotalText [3] (vs. 80. 9% of fully supervised counterpart), 31. 1% better than training directly with upright bounding box annotations, and saves 80%+ labeling costs.
6 code implementations • CVPR 2021 • Peize Sun, Rufeng Zhang, Yi Jiang, Tao Kong, Chenfeng Xu, Wei Zhan, Masayoshi Tomizuka, Lei LI, Zehuan Yuan, Changhu Wang, Ping Luo
In our method, however, a fixed sparse set of learned object proposals, total length of $N$, are provided to object recognition head to perform classification and location.
Ranked #91 on
Object Detection
on COCO minival
1 code implementation • ICLR 2021 • Xingang Pan, Bo Dai, Ziwei Liu, Chen Change Loy, Ping Luo
Through our investigation, we found that such a pre-trained GAN indeed contains rich 3D knowledge and thus can be used to recover 3D shape from a single 2D image in an unsupervised manner.
no code implementations • 16 Sep 2020 • Yuanfeng Ji, Ruimao Zhang, Zhen Li, Jiamin Ren, Shaoting Zhang, Ping Luo
Unlike the recent neural architecture search (NAS) methods that typically searched the optimal operators in each network layer, but missed a good strategy to search for feature aggregations, this paper proposes a novel NAS method for 3D medical image segmentation, named UXNet, which searches both the scale-wise feature aggregation strategies as well as the block-wise operators in the encoder-decoder network.
2 code implementations • 14 Sep 2020 • Hao Tan, Ran Cheng, Shihua Huang, Cheng He, Changxiao Qiu, Fan Yang, Ping Luo
Despite the remarkable successes of Convolutional Neural Networks (CNNs) in computer vision, it is time-consuming and error-prone to manually design a CNN.
no code implementations • 27 Aug 2020 • Zhibo Zou, Jun-Jie Huang, Ping Luo
Based on simple and traditional methods, we propose a compensation tracker to further alleviate the lost tracking problem caused by missing detection.
3 code implementations • ECCV 2020 • Jingkang Yang, Litong Feng, Weirong Chen, Xiaopeng Yan, Huabin Zheng, Ping Luo, Wayne Zhang
Therefore, a simple yet effective WSL framework is proposed.
Ranked #5 on
Image Classification
on WebVision-1000
no code implementations • ECCV 2020 • Chaofan Tao, Qinhong Jiang, Lixin Duan, Ping Luo
Existing work addressed this challenge by either learning social spatial interactions represented by the positions of a group of pedestrians, while ignoring their temporal coherence (\textit{i. e.} dependencies between different long trajectories), or by understanding the complicated scene layout (\textit{e. g.} scene segmentation) to ensure safe navigation.
2 code implementations • ECCV 2020 • Wenhai Wang, Xuebo Liu, Xiaozhong Ji, Enze Xie, Ding Liang, Zhibo Yang, Tong Lu, Chunhua Shen, Ping Luo
Unlike previous works that merely employed visual features for text detection, this work proposes a novel text spotter, named Ambiguity Eliminating Text Spotter (AE TextSpotter), which learns both visual and linguistic features to significantly reduce ambiguity in text detection.
2 code implementations • ECCV 2020 • Sheng Jin, Lumin Xu, Jin Xu, Can Wang, Wentao Liu, Chen Qian, Wanli Ouyang, Ping Luo
This paper investigates the task of 2D human whole-body pose estimation, which aims to localize dense landmarks on the entire human body including face, hands, body, and feet.
Ranked #6 on
2D Human Pose Estimation
on COCO-WholeBody
no code implementations • ECCV 2020 • Sheng Jin, Wentao Liu, Enze Xie, Wenhai Wang, Chen Qian, Wanli Ouyang, Ping Luo
The modules of HGG can be trained end-to-end with the keypoint detection network and is able to supervise the grouping process in a hierarchical manner.
Ranked #3 on
Keypoint Detection
on OCHuman
3 code implementations • CVPR 2020 • Wang Zeng, Wanli Ouyang, Ping Luo, Wentao Liu, Xiaogang Wang
This paper proposes a model-free 3D human mesh estimation framework, named DecoMR, which explicitly establishes the dense correspondence between the mesh and the local image features in the UV space (i. e. a 2D space used for texture mapping of 3D mesh).
Ranked #1 on
3D Human Reconstruction
on Surreal
1 code implementation • CVPR 2020 • Zhouxia Wang, Jiawei Zhang, Mude Lin, Jiong Wang, Ping Luo, Jimmy Ren
Automatically selecting exposure bracketing (images exposed differently) is important to obtain a high dynamic range image by using multi-exposure fusion.
no code implementations • 24 Apr 2020 • Zhongzhan Huang, Wenqi Shao, Xinjiang Wang, Liang Lin, Ping Luo
Channel pruning is a popular technique for compressing convolutional neural networks (CNNs), where various pruning criteria have been proposed to remove the redundant filters.
1 code implementation • 21 Apr 2020 • Wenjie Li, Zhaoyang Zhang, Xinjiang Wang, Ping Luo
Although adaptive optimization algorithms such as Adam show fast convergence in many machine learning tasks, this paper identifies a problem of Adam by analyzing its performance in a simple non-convex synthetic problem, showing that Adam's fast convergence would possibly lead the algorithm to local minimums.
1 code implementation • ECCV 2020 • Enze Xie, Wenjia Wang, Wenhai Wang, Mingyu Ding, Chunhua Shen, Ping Luo
To address this important problem, this work proposes a large-scale dataset for transparent object segmentation, named Trans10K, consisting of 10, 428 images of real scenarios with carefully manual annotations, which are 10 times larger than the existing datasets.
Ranked #4 on
Semantic Segmentation
on Trans10K
1 code implementation • ECCV 2020 • Xingang Pan, Xiaohang Zhan, Bo Dai, Dahua Lin, Chen Change Loy, Ping Luo
Learning a good image prior is a long-term goal for image restoration and manipulation.
1 code implementation • 19 Mar 2020 • An Zhao, Mingyu Ding, Zhiwu Lu, Tao Xiang, Yulei Niu, Jiechao Guan, Ji-Rong Wen, Ping Luo
Existing few-shot learning (FSL) methods make the implicit assumption that the few target class samples are from the same domain as the source class samples.
no code implementations • CVPR 2020 • Ruimao Zhang, Zhanglin Peng, Lingyun Wu, Zhen Li, Ping Luo
This work investigates a novel dynamic learning-to-normalize (L2N) problem by proposing Exemplar Normalization (EN), which is able to learn different normalization methods for different convolutional layers and image samples of a deep network.
3 code implementations • 12 Mar 2020 • Han Yang, Ruimao Zhang, Xiaobao Guo, Wei Liu, WangMeng Zuo, Ping Luo
First, a semantic layout generation module utilizes semantic segmentation of the reference image to progressively predict the desired semantic layout after try-on.
Ranked #4 on
Virtual Try-on
on VITON
(IS metric)
1 code implementation • ICML 2020 • Wenqi Shao, Shitao Tang, Xingang Pan, Ping Tan, Xiaogang Wang, Ping Luo
Unlike prior arts that simply removed the inhibited channels, we propose to "wake them up" during training by designing a novel neural building block, termed Channel Equilibrium (CE) block, which enables channels at the same layer to contribute equally to the learned representation.
no code implementations • 30 Jan 2020 • Sheng Zhou, Xinjiang Wang, Ping Luo, Litong Feng, Wenjie Li, Wei zhang
This phenomenon is caused by the normalization effect of BN, which induces a non-trainable region in the parameter space and reduces the network capacity as a result.
2 code implementations • CVPR 2020 • Mingyu Ding, Yuqi Huo, Hongwei Yi, Zhe Wang, Jianping Shi, Zhiwu Lu, Ping Luo
3D object detection from a single image without LiDAR is a challenging task due to the lack of accurate depth information.
Ranked #17 on
Monocular 3D Object Detection
on KITTI Cars Moderate
(using extra training data)
no code implementations • 28 Nov 2019 • Mingyu Ding, Zhe Wang, Bolei Zhou, Jianping Shi, Zhiwu Lu, Ping Luo
Moreover, our framework is able to utilize both labeled and unlabeled frames in the video through joint training, while no additional calculation is required in inference.
no code implementations • ICCV 2019 • Hang Zhou, Ziwei Liu, Xudong Xu, Ping Luo, Xiaogang Wang
Extensive experiments demonstrate that our framework is capable of inpainting realistic and varying audio segments with or without visual contexts.
2 code implementations • CVPR 2020 • Enze Xie, Peize Sun, Xiaoge Song, Wenhai Wang, Ding Liang, Chunhua Shen, Ping Luo
In this paper, we introduce an anchor-box free and single shot instance segmentation method, which is conceptually simple, fully convolutional and can be used as a mask prediction module for instance segmentation, by easily embedding it into most off-the-shelf detection methods.
Ranked #96 on
Instance Segmentation
on COCO test-dev
no code implementations • 25 Sep 2019 • Wenqi Shao, Shitao Tang, Xingang Pan, Ping Tan, Xiaogang Wang, Ping Luo
However, over-sparse CNNs have many collapsed channels (i. e. many channels with undesired zero values), impeding their learning ability.
1 code implementation • 16 Sep 2019 • Wenjia Wang, Enze Xie, Peize Sun, Wenhai Wang, Lixun Tian, Chunhua Shen, Ping Luo
Nonetheless, most of the previous methods may not work well in recognizing text with low resolution which is often seen in natural scene images.
no code implementations • 11 Sep 2019 • Hang Yu, Aishan Liu, Xianglong Liu, Gengchao Li, Ping Luo, Ran Cheng, Jichen Yang, Chongzhi Zhang
In other words, DNNs trained with PDA are able to obtain more robustness against both adversarial attacks as well as common corruptions than the recent state-of-the-art methods.
no code implementations • 31 Aug 2019 • Zhuoran Yu, Aojun Zhou, Yukun Ma, Yudian Li, Xiaohan Zhang, Ping Luo
Experiment results show that SCT improves accuracy of single Resnet-50 on ImageNet by 1. 7% and 11. 5% accuracy when testing on image sizes of 224 and 128 respectively.
no code implementations • ICCV 2019 • Zhanghui Kuang, Yiming Gao, Guanbin Li, Ping Luo, Yimin Chen, Liang Lin, Wayne Zhang
To address this issue, we propose a novel Graph Reasoning Network (GRNet) on a Similarity Pyramid, which learns similarities between a query and a gallery cloth by using both global and local representations in multiple scales.
Ranked #4 on
Image Retrieval
on DeepFashion - Consumer-to-shop
(Rank-1 metric)
no code implementations • ICCV 2019 • Zhaoyang Zhang, Jingyu Li, Wenqi Shao, Zhanglin Peng, Ruimao Zhang, Xiaogang Wang, Ping Luo
ResNeXt, still suffers from the sub-optimal performance due to manually defining the number of groups as a constant over all of the layers.
no code implementations • ICCV 2019 • Jiangfan Han, Xiaoyi Dong, Ruimao Zhang, Dong-Dong Chen, Weiming Zhang, Nenghai Yu, Ping Luo, Xiaogang Wang
Recently, generation-based methods have received much attention since they directly use feed-forward networks to generate the adversarial samples, which avoid the time-consuming iterative attacking procedure in optimization-based and gradient-based methods.
no code implementations • ICCV 2019 • Jiangfan Han, Ping Luo, Xiaogang Wang
Unlike previous works constrained by many conditions, making them infeasible to real noisy cases, this work presents a novel deep self-learning framework to train a robust network on the real noisy datasets without extra supervision.
6 code implementations • CVPR 2020 • Cheng-Han Lee, Ziwei Liu, Lingyun Wu, Ping Luo
To overcome these drawbacks, we propose a novel framework termed MaskGAN, enabling diverse and interactive face manipulation.
no code implementations • 22 Jul 2019 • Ping Luo, Ruimao Zhang, Jiamin Ren, Zhanglin Peng, Jingyu Li
Analyses of SN are also presented to answer the following three questions: (a) Is it useful to allow each normalization layer to select its own normalizer?
no code implementations • 14 May 2019 • Ganbin Zhou, Ping Luo, Jingwu Chen, Fen Lin, Leyu Lin, Qing He
To enrich the generated responses, ARM introduces a large number of molecule-mechanisms as various responding styles, which are conducted by taking different combinations from a few atom-mechanisms.
1 code implementation • ICCV 2019 • Xingang Pan, Xiaohang Zhan, Jianping Shi, Xiaoou Tang, Ping Luo
Unlike existing works that design normalization techniques for specific tasks, we propose Switchable Whitening (SW), which provides a general form unifying different whitening methods as well as standardization methods.
1 code implementation • CVPR 2019 • Wenqi Shao, Tianjian Meng, Jingyu Li, Ruimao Zhang, Yudian Li, Xiaogang Wang, Ping Luo
Unlike $\ell_1$ and $\ell_0$ constraints that impose difficulties in optimization, we turn this constrained optimization problem into feed-forward computation by proposing SparsestMax, which is a sparse version of softmax.
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.
5 code implementations • CVPR 2019 • Yuying Ge, Ruimao Zhang, Lingyun Wu, Xiaogang Wang, Xiaoou Tang, Ping Luo
A strong baseline is proposed, called Match R-CNN, which builds upon Mask R-CNN to solve the above four tasks in an end-to-end manner.
no code implementations • 4 Dec 2018 • Yujun Shen, Bolei Zhou, Ping Luo, Xiaoou Tang
In the second stage, they compete in the image domain to render photo-realistic images that contain high diversity but preserve identity.
no code implementations • NeurIPS 2018 • Guangrun Wang, Jiefeng Peng, Ping Luo, Xinjiang Wang, Liang Lin
In this paper, we present a novel normalization method, called Kalman Normalization (KN), for improving and accelerating the training of DNNs, particularly under the context of micro-batches.
no code implementations • 19 Nov 2018 • Ping Luo, Zhanglin Peng, Jiamin Ren, Ruimao Zhang
Our results suggest that (1) using distinct normalizers improves both learning and generalization of a ConvNet; (2) the choices of normalizers are more related to depth and batch size, but less relevant to parameter initialization, learning rate decay, and solver; (3) different tasks and datasets have different behaviors when learning to select normalizers.
1 code implementation • ICLR 2019 • Ping Luo, Xinjiang Wang, Wenqi Shao, Zhanglin Peng
Batch Normalization (BN) improves both convergence and generalization in training neural networks.
no code implementations • 22 Aug 2018 • Ganbin Zhou, Rongyu Cao, Xiang Ao, Ping Luo, Fen Lin, Leyu Lin, Qing He
Additionally, a "low-level sharing, high-level splitting" structure of CNN is designed to handle the documents from different content domains.
no code implementations • 15 Aug 2018 • Zhaoyang Zhang, Zhanghui Kuang, Ping Luo, Litong Feng, Wei zhang
Secondly, TSD significantly reduces the computations to run video action recognition with compressed frames on the cloud, while maintaining high recognition accuracies.
16 code implementations • ECCV 2018 • Xingang Pan, Ping Luo, Jianping Shi, Xiaoou Tang
IBN-Net carefully integrates Instance Normalization (IN) and Batch Normalization (BN) as building blocks, and can be wrapped into many advanced deep networks to improve their performances.
1 code implementation • 20 Jul 2018 • Hang Zhou, Yu Liu, Ziwei Liu, Ping Luo, Xiaogang Wang
Talking face generation aims to synthesize a sequence of face images that correspond to a clip of speech.
no code implementations • 16 Jul 2018 • Ruimao Zhang, Hongbin Sun, Jingyu Li, Yuying Ge, Liang Lin, Ping Luo, Xiaogang Wang
To address the above issues, we present a novel and practical deep architecture for video person re-identification termed Self-and-Collaborative Attention Network (SCAN).
3 code implementations • ICLR 2019 • Ping Luo, Jiamin Ren, Zhanglin Peng, Ruimao Zhang, Jingyu Li
We hope SN will help ease the usage and understand the normalization techniques in deep learning.