no code implementations • ACL (WebNLG, INLG) 2020 • Qipeng Guo, Zhijing Jin, Ning Dai, Xipeng Qiu, xiangyang xue, David Wipf, Zheng Zhang
Text verbalization of knowledge graphs is an important problem with wide application to natural language generation (NLG) systems.
no code implementations • 17 Jun 2022 • Yifeng Zhuang, Qiang Sun, Yanwei Fu, Lifeng Chen, xiangyang xue
Since the attention mechanism in the transformer architecture can better integrate inter- and intra-modal information of vision and language.
no code implementations • 9 May 2022 • Haitao Lin, Chilam Cheang, Yanwei Fu, xiangyang xue
The physical robot experiments confirm the utility of our method in object-cluttered scenes.
no code implementations • 9 May 2022 • Chilam Cheang, Haitao Lin, Yanwei Fu, xiangyang xue
This paper studies the task of any objects grasping from the known categories by free-form language instructions.
no code implementations • CVPR 2022 • Yun He, Xinlin Ren, Danhang Tang, yinda zhang, xiangyang xue, Yanwei Fu
To address this, we propose a novel deep point cloud compression method that preserves local density information.
no code implementations • 26 Apr 2022 • Shangchao Su, Bin Li, xiangyang xue
Federated Learning (FL) has recently made significant progress as a new machine learning paradigm for privacy protection.
no code implementations • 21 Apr 2022 • Chao Wen, yinda zhang, Chenjie Cao, Zhuwen Li, xiangyang xue, Yanwei Fu
We study the problem of shape generation in 3D mesh representation from a small number of color images with or without camera poses.
no code implementations • CVPR 2022 • Wenxuan Wang, Xuelin Qian, Yanwei Fu, xiangyang xue
With the wide applications of deep neural network models in various computer vision tasks, more and more works study the model vulnerability to adversarial examples.
no code implementations • 31 Mar 2022 • Xuelin Qian, Li Wang, Yi Zhu, Li Zhang, Yanwei Fu, xiangyang xue
Conventional 3D object detection approaches concentrate on bounding boxes representation learning with several parameters, i. e., localization, dimension, and orientation.
1 code implementation • 24 Mar 2022 • Likun Cai, Zhi Zhang, Yi Zhu, Li Zhang, Mu Li, xiangyang xue
Multiple datasets and open challenges for object detection have been introduced in recent years.
Ranked #1 on
Object Detection
on BigDetection val
no code implementations • 22 Mar 2022 • Yuxin Hong, Xuelin Qian, Simian Luo, xiangyang xue, Yanwei Fu
To this end, this paper proposes a novel model of learning to Quantize, Scrabble, and Craft (QS-Craft) for conditional human motion animation.
no code implementations • CVPR 2022 • Boyan Jiang, yinda zhang, Xingkui Wei, xiangyang xue, Yanwei Fu
A simple yet effective linear motion model is proposed to provide a rough and regularized motion estimation, followed by per-frame compensation for pose and geometry details with the residual encoded in the auxiliary code.
no code implementations • 15 Feb 2022 • Jinyang Yuan, Tonglin Chen, Bin Li, xiangyang xue
Visual scene representation learning is an important research problem in the field of computer vision.
1 code implementation • 30 Dec 2021 • Jingye Chen, Haiyang Yu, jianqi ma, Mengnan Guan, Xixi Xu, Xiaocong Wang, Shaobo Qu, Bin Li, xiangyang xue
Based on our observations, we attribute the scarce attention on Chinese text recognition to the lack of reasonable dataset construction standards, unified evaluation methods, and results of the existing baselines.
no code implementations • ICCV 2021 • Zhikang Zou, Xiaoqing Ye, Liang Du, Xianhui Cheng, Xiao Tan, Li Zhang, Jianfeng Feng, xiangyang xue, Errui Ding
Low-cost monocular 3D object detection plays a fundamental role in autonomous driving, whereas its accuracy is still far from satisfactory.
1 code implementation • 13 Dec 2021 • Jingye Chen, Haiyang Yu, jianqi ma, Bin Li, xiangyang xue
However, the recognition of low-resolution scene text images remains a challenge.
no code implementations • 7 Dec 2021 • Jinyang Yuan, Bin Li, xiangyang xue
When observing a visual scene that contains multiple objects from multiple viewpoints, humans are able to perceive the scene in a compositional way from each viewpoint, while achieving the so-called "object constancy" across different viewpoints, even though the exact viewpoints are untold.
1 code implementation • 3 Dec 2021 • Zheyuan Zhou, Liang Du, Xiaoqing Ye, Zhikang Zou, Xiao Tan, Li Zhang, xiangyang xue, Jianfeng Feng
Monocular 3D object detection aims to predict the object location, dimension and orientation in 3D space alongside the object category given only a monocular image.
no code implementations • 18 Sep 2021 • Yanwei Fu, Feng Li, Paula boned Fustel, Lei Zhao, Lijie Jia, Haojie Zheng, Qiang Sun, Shisong Rong, Haicheng Tang, xiangyang xue, Li Yang, Hong Li, Jiao Xie Wenxuan Wang, Yuan Li, Wei Wang, Yantao Pei, Jianmin Wang, Xiuqi Wu, Yanhua Zheng, Hongxia Tian, Mengwei Gu
The image-level performance of COVID-19 prescreening model in the China-Spain multicenter study achieved an AUC of 0. 913 (95% CI, 0. 898-0. 927), with a sensitivity of 0. 695 (95% CI, 0. 643-0. 748), a specificity of 0. 904 (95% CI, 0. 891 -0. 919), an accuracy of 0. 875(0. 861-0. 889), and a F1 of 0. 611(0. 568-0. 655).
1 code implementation • NeurIPS 2021 • Li Wang, Li Zhang, Yi Zhu, Zhi Zhang, Tong He, Mu Li, xiangyang xue
Recognizing and localizing objects in the 3D space is a crucial ability for an AI agent to perceive its surrounding environment.
no code implementations • CVPR 2022 • Haitao Lin, Zichang Liu, Chilam Cheang, Yanwei Fu, Guodong Guo, xiangyang xue
The concatenation of the observed point cloud and symmetric one reconstructs a coarse object shape, thus facilitating object center (3D translation) and 3D size estimation.
1 code implementation • 22 Jun 2021 • Jingye Chen, Bin Li, xiangyang xue
Inspired by the fact that humans can generalize to know how to write characters unseen before if they have learned stroke orders of some characters, we propose a stroke-based method by decomposing each character into a sequence of strokes, which are the most basic units of Chinese characters.
1 code implementation • CVPR 2021 • Jingye Chen, Bin Li, xiangyang xue
Image super-resolution, which is often regarded as a preprocessing procedure of scene text recognition, aims to recover the realistic features from a low-resolution text image.
no code implementations • 12 Jun 2021 • Yanwei Fu, Lei Zhao, Haojie Zheng, Qiang Sun, Li Yang, Hong Li, Jiao Xie, xiangyang xue, Feng Li, Yuan Li, Wei Wang, Yantao Pei, Jianmin Wang, Xiuqi Wu, Yanhua Zheng, Hongxia Tian Mengwei Gu1
It is still nontrivial to develop a new fast COVID-19 screening method with the easier access and lower cost, due to the technical and cost limitations of the current testing methods in the medical resource-poor districts.
1 code implementation • NeurIPS 2021 • Chenjie Cao, Yuxin Hong, Xiang Li, Chengrong Wang, Chengming Xu, xiangyang xue, Yanwei Fu
To address these limitations, we propose a novel model -- image Local Autoregressive Transformer (iLAT), to better facilitate the locally guided image synthesis.
no code implementations • CVPR 2021 • Wenxuan Wang, Bangjie Yin, Taiping Yao, Li Zhang, Yanwei Fu, Shouhong Ding, Jilin Li, Feiyue Huang, xiangyang xue
Previous substitute training approaches focus on stealing the knowledge of the target model based on real training data or synthetic data, without exploring what kind of data can further improve the transferability between the substitute and target models.
1 code implementation • CVPR 2021 • Li Wang, Liang Du, Xiaoqing Ye, Yanwei Fu, Guodong Guo, xiangyang xue, Jianfeng Feng, Li Zhang
The objective of this paper is to learn context- and depth-aware feature representation to solve the problem of monocular 3D object detection.
Ranked #8 on
Monocular 3D Object Detection
on KITTI Cars Moderate
2 code implementations • CVPR 2021 • Chengming Xu, Chen Liu, Li Zhang, Chengjie Wang, Jilin Li, Feiyue Huang, xiangyang xue, Yanwei Fu
Our insight is that these methods would lead to poor adaptation with redundant matching, and leveraging channel-wise adjustment is the key to well adapting the learned knowledge to new classes.
1 code implementation • 22 Mar 2021 • Fan Shi, Bin Li, xiangyang xue
In this paper we aim to solve the latter one by proposing a deep latent variable model, in which multiple Gaussian processes are employed as priors of latent variables to separately learn underlying abstract concepts from RPMs; thus the proposed model is interpretable in terms of concept-specific latent variables.
1 code implementation • 19 Mar 2021 • Jinyang Yuan, Bin Li, xiangyang xue
The proposed ADI framework focuses on the acquisition and utilization of knowledge, and is complementary to existing deep generative models proposed for compositional scene representation.
no code implementations • CVPR 2021 • Boyan Jiang, yinda zhang, Xingkui Wei, xiangyang xue, Yanwei Fu
To model the motion, a neural Ordinary Differential Equation (ODE) is trained to update the initial state conditioned on the learned motion code, and a decoder takes the shape code and the updated state code to reconstruct the 3D model at each time stamp.
no code implementations • 10 Feb 2021 • Tairu Qiu, Guanxian Chen, Zhongang Qi, Bin Li, Ying Shan, xiangyang xue
Short video applications like TikTok and Kwai have been a great hit recently.
1 code implementation • NeurIPS 2020 • Jie Shao, Kai Hu, Changhu Wang, xiangyang xue, Bhiksha Raj
In this paper, we study what would happen when normalization layers are removed from the network, and show how to train deep neural networks without normalization layers and without performance degradation.
no code implementations • 27 Oct 2020 • Xuli Shen, Qing Xu, xiangyang xue
and the mean value of loss function is used as the empirical risk by Law of Large Numbers (LLN).
no code implementations • 7 Oct 2020 • Xuelin Qian, Huazhu Fu, Weiya Shi, Tao Chen, Yanwei Fu, Fei Shan, xiangyang xue
To counter the outbreak of COVID-19, the accurate diagnosis of suspected cases plays a crucial role in timely quarantine, medical treatment, and preventing the spread of the pandemic.
no code implementations • 4 Sep 2020 • Yanwei Fu, Feng Li, Wenxuan Wang, Haicheng Tang, Xuelin Qian, Mengwei Gu, xiangyang xue
After more than four months study, we found that the confirmed cases of COVID-19 present the consistent ocular pathological symbols; and we propose a new screening method of analyzing the eye-region images, captured by common CCD and CMOS cameras, could reliably make a rapid risk screening of COVID-19 with very high accuracy.
1 code implementation • 4 Aug 2020 • Jie Shao, Xin Wen, Bingchen Zhao, xiangyang xue
The current research focus on Content-Based Video Retrieval requires higher-level video representation describing the long-range semantic dependencies of relevant incidents, events, etc.
no code implementations • 26 May 2020 • Xuelin Qian, Wenxuan Wang, Li Zhang, Fangrui Zhu, Yanwei Fu, Tao Xiang, Yu-Gang Jiang, xiangyang xue
Specifically, we consider that under cloth-changes, soft-biometrics such as body shape would be more reliable.
1 code implementation • CVPR 2020 • Hangyu Lin, Yanwei Fu, Yu-Gang Jiang, xiangyang xue
Unfortunately, the representation learned by SketchRNN is primarily for the generation tasks, rather than the other tasks of recognition and retrieval of sketches.
no code implementations • 19 May 2020 • Xixi Xu, Chao Lu, Liang Zhu, xiangyang xue, Guanxian Chen, Qi Guo, Yining Lin, Zhijian Zhao
Most modern Multi-Object Tracking (MOT) systems typically apply REID-based paradigm to hold a balance between computational efficiency and performance.
3 code implementations • EMNLP 2020 • Linyang Li, Ruotian Ma, Qipeng Guo, xiangyang xue, Xipeng Qiu
Adversarial attacks for discrete data (such as texts) have been proved significantly more challenging than continuous data (such as images) since it is difficult to generate adversarial samples with gradient-based methods.
1 code implementation • CVPR 2020 • Jiashun Wang, Chao Wen, Yanwei Fu, Haitao Lin, Tianyun Zou, xiangyang xue, yinda zhang
Pose transfer has been studied for decades, in which the pose of a source mesh is applied to a target mesh.
no code implementations • 1 Mar 2020 • Liang Du, Jingang Tan, xiangyang xue, Lili Chen, Hongkai Wen, Jianfeng Feng, Jiamao Li, Xiaolin Zhang
We propose a novel fast and robust 3D point clouds segmentation framework via coupled feature selection, named 3DCFS, that jointly performs semantic and instance segmentation.
no code implementations • 17 Jan 2020 • Wenxuan Wang, Yanwei Fu, Qiang Sun, Tao Chen, Chenjie Cao, Ziqi Zheng, Guoqiang Xu, Han Qiu, Yu-Gang Jiang, xiangyang xue
Considering the phenomenon of uneven data distribution and lack of samples is common in real-world scenarios, we further evaluate several tasks of few-shot expression learning by virtue of our F2ED, which are to recognize the facial expressions given only few training instances.
1 code implementation • ECCV 2020 • Xingkui Wei, yinda zhang, Zhuwen Li, Yanwei Fu, xiangyang xue
The explicit constraints on both depth (structure) and pose (motion), when combined with the learning components, bring the merit from both traditional BA and emerging deep learning technology.
no code implementations • 2 Dec 2019 • Qipeng Guo, Xipeng Qiu, PengFei Liu, xiangyang xue, Zheng Zhang
In this paper, we introduce the prior knowledge, multi-scale structure, into self-attention modules.
2 code implementations • 23 Nov 2019 • Kaiqiang Song, Logan Lebanoff, Qipeng Guo, Xipeng Qiu, xiangyang xue, Chen Li, Dong Yu, Fei Liu
If generating a word can introduce an erroneous relation to the summary, the behavior must be discouraged.
Ranked #23 on
Text Summarization
on GigaWord
no code implementations • 17 Nov 2019 • Yiyao Shi, Jian Wang, xiangyang xue
In this paper, a learning-free color constancy algorithm called the Patch-wise Bright Pixels (PBP) is proposed.
no code implementations • ICLR 2020 • Xisen Jin, Zhongyu Wei, Junyi Du, xiangyang xue, Xiang Ren
Human and metrics evaluation on both LSTM models and BERT Transformer models on multiple datasets show that our algorithms outperform prior hierarchical explanation algorithms.
no code implementations • 25 Sep 2019 • Qiang Sun, Zhinan Cheng, Yanwei Fu, Wenxuan Wang, Yu-Gang Jiang, xiangyang xue
Instead of learning the cross features directly, DeepEnFM adopts the Transformer encoder as a backbone to align the feature embeddings with the clues of other fields.
1 code implementation • CVPR 2020 • Li Zhang, Mohan Chen, Anurag Arnab, xiangyang xue, Philip H. S. Torr
A fully-connected graph, such as the self-attention operation in Transformers, is beneficial for such modelling, however, its computational overhead is prohibitive.
no code implementations • CVPR 2019 • Zhiqiang Shen, Mingyang Huang, Jianping Shi, xiangyang xue, Thomas Huang
The proposed INIT exhibits three import advantages: (1) the instance-level objective loss can help learn a more accurate reconstruction and incorporate diverse attributes of objects; (2) the styles used for target domain of local/global areas are from corresponding spatial regions in source domain, which intuitively is a more reasonable mapping; (3) the joint training process can benefit both fine and coarse granularity and incorporates instance information to improve the quality of global translation.
no code implementations • ICLR 2019 • Yanwei Fu, Shun Zhang, Donghao Li, Xinwei Sun, xiangyang xue, Yuan YAO
This paper proposes a Pruning in Training (PiT) framework of learning to reduce the parameter size of networks.
no code implementations • 17 Apr 2019 • Yanze Wu, Qiang Sun, Jianqi Ma, Bin Li, Yanwei Fu, Yao Peng, xiangyang xue
Particularly, The QGMRN is composed of visual, textual and routing network.
1 code implementation • 25 Mar 2019 • Li Wang, Yongbo Li, xiangyang xue
Extensive experiments demonstrate that our network produces much better results on unseen datasets compared with existing counting adaption models.
2 code implementations • NAACL 2019 • Qipeng Guo, Xipeng Qiu, PengFei Liu, Yunfan Shao, xiangyang xue, Zheng Zhang
Although Transformer has achieved great successes on many NLP tasks, its heavy structure with fully-connected attention connections leads to dependencies on large training data.
Ranked #10 on
Sentiment Analysis
on SST-5 Fine-grained classification
1 code implementation • 7 Feb 2019 • Jinyang Yuan, Bin Li, xiangyang xue
Different from existing methods, the proposed method disentangles the attributes of an object into ``shape'' and ``appearance'' which are modeled separately by the mixture weights and the mixture components.
1 code implementation • 21 Dec 2018 • Guoyun Tu, Yanwei Fu, Boyang Li, Jiarui Gao, Yu-Gang Jiang, xiangyang xue
However, the sparsity of emotional expressions in the videos poses an obstacle to visual emotion analysis.
1 code implementation • 6 Dec 2018 • Zhiqiang Shen, Zhankui He, xiangyang xue
In this paper, we present a method for compressing large, complex trained ensembles into a single network, where knowledge from a variety of trained deep neural networks (DNNs) is distilled and transferred to a single DNN.
no code implementations • 28 Nov 2018 • Peng Lu, Hangyu Lin, Yanwei Fu, Shaogang Gong, Yu-Gang Jiang, xiangyang xue
Additionally, to study the tasks of sketch-based hairstyle retrieval, this paper contributes a new instance-level photo-sketch dataset - Hairstyle Photo-Sketch dataset, which is composed of 3600 sketches and photos, and 2400 sketch-photo pairs.
no code implementations • ICLR 2019 • Hanze Dong, Yanwei Fu, Sung Ju Hwang, Leonid Sigal, xiangyang xue
This paper studies the problem of Generalized Zero-shot Learning (G-ZSL), whose goal is to classify instances belonging to both seen and unseen classes at the test time.
1 code implementation • 25 Sep 2018 • Zhiqiang Shen, Zhuang Liu, Jianguo Li, Yu-Gang Jiang, Yurong Chen, xiangyang xue
Thus, a better solution to handle these critical problems is to train object detectors from scratch, which motivates our proposed method.
no code implementations • 14 Aug 2018 • Qipeng Guo, Xipeng Qiu, xiangyang xue, Zheng Zhang
Text generation is a fundamental building block in natural language processing tasks.
no code implementations • 14 Jun 2018 • Huiyuan Zhuo, Xuelin Qian, Yanwei Fu, Heng Yang, xiangyang xue
In this paper, we proposed a novel filter pruning for convolutional neural networks compression, namely spectral clustering filter pruning with soft self-adaption manners (SCSP).
1 code implementation • 15 Apr 2018 • Zitian Chen, Yanwei Fu, yinda zhang, Yu-Gang Jiang, xiangyang xue, Leonid Sigal
In semantic space, we search for related concepts, which are then projected back into the image feature spaces by the decoder portion of the TriNet.
1 code implementation • 8 Feb 2018 • Chengming Xu, Yanwei Fu, Bing Zhang, Zitian Chen, Yu-Gang Jiang, xiangyang xue
This paper targets at learning to score the figure skating sports videos.
no code implementations • ICLR 2018 • jianqi ma, Hangyu Lin, yinda zhang, Yanwei Fu, xiangyang xue
Besides directly augmenting image features, we transform the image features to semantic space using the encoder and perform the data augmentation.
2 code implementations • ECCV 2018 • Xuelin Qian, Yanwei Fu, Tao Xiang, Wenxuan Wang, Jie Qiu, Yang Wu, Yu-Gang Jiang, xiangyang xue
Person Re-identification (re-id) faces two major challenges: the lack of cross-view paired training data and learning discriminative identity-sensitive and view-invariant features in the presence of large pose variations.
no code implementations • 29 Nov 2017 • Qingfu Wan, Wei zhang, xiangyang xue
For the first time, we show that training regression network from skeleton map alone is capable of meeting the performance of state-of-theart 3D human pose estimation works.
no code implementations • CVPR 2018 • Changmao Cheng, Yanwei Fu, Yu-Gang Jiang, Wei Liu, Wenlian Lu, Jianfeng Feng, xiangyang xue
Inspired by the recent neuroscience studies on the left-right asymmetry of the human brain in processing low and high spatial frequency information, this paper introduces a dual skipping network which carries out coarse-to-fine object categorization.
no code implementations • 13 Oct 2017 • Yanwei Fu, Tao Xiang, Yu-Gang Jiang, xiangyang xue, Leonid Sigal, Shaogang Gong
With the recent renaissance of deep convolution neural networks, encouraging breakthroughs have been achieved on the supervised recognition tasks, where each class has sufficient training data and fully annotated training data.
no code implementations • ICCV 2017 • Xuelin Qian, Yanwei Fu, Yu-Gang Jiang, Tao Xiang, xiangyang xue
Our model is able to learn deep discriminative feature representations at different scales and automatically determine the most suitable scales for matching.
4 code implementations • ICCV 2017 • Zhiqiang Shen, Zhuang Liu, Jianguo Li, Yu-Gang Jiang, Yurong Chen, xiangyang xue
State-of-the-art object objectors rely heavily on the off-the-shelf networks pre-trained on large-scale classification datasets like ImageNet, which incurs learning bias due to the difference on both the loss functions and the category distributions between classification and detection tasks.
no code implementations • 27 Jul 2017 • Keke He, Yanwei Fu, xiangyang xue
Facial attribute analysis in the real world scenario is very challenging mainly because of complex face variations.
no code implementations • 14 Jun 2017 • Yu-Gang Jiang, Zuxuan Wu, Jinhui Tang, Zechao Li, xiangyang xue, Shih-Fu Chang
More specifically, we utilize three Convolutional Neural Networks (CNNs) operating on appearance, motion and audio signals to extract their corresponding features.
no code implementations • 28 May 2017 • Yanwei Fu, Hanze Dong, Yu-feng Ma, Zhengjun Zhang, xiangyang xue
To solve this problem, we propose the Extreme Value Learning (EVL) formulation to learn the mapping from visual feature to semantic space.
5 code implementations • ICCV 2017 • Xingyi Zhou, Qi-Xing Huang, Xiao Sun, xiangyang xue, Yichen Wei
We propose a weakly-supervised transfer learning method that uses mixed 2D and 3D labels in a unified deep neutral network that presents two-stage cascaded structure.
3D Multi-Person Pose Estimation (absolute)
3D Multi-Person Pose Estimation (root-relative)
+3
no code implementations • 7 Apr 2017 • Weidong Yin, Yanwei Fu, Leonid Sigal, xiangyang xue
Generating and manipulating human facial images using high-level attributal controls are important and interesting problems.
no code implementations • CVPR 2017 • Zhiqiang Shen, Jianguo Li, Zhou Su, Minjun Li, Yurong Chen, Yu-Gang Jiang, xiangyang xue
This paper focuses on a novel and challenging vision task, dense video captioning, which aims to automatically describe a video clip with multiple informative and diverse caption sentences.
no code implementations • 29 Mar 2017 • Zhiqiang Shen, Yu-Gang Jiang, Dequan Wang, xiangyang xue
On both datasets, we achieve better results than many state-of-the-art approaches, including a few using oracle (manually annotated) bounding boxes in the test images.
4 code implementations • 3 Mar 2017 • Jianqi Ma, Weiyuan Shao, Hao Ye, Li Wang, Hong Wang, Yingbin Zheng, xiangyang xue
This paper introduces a novel rotation-based framework for arbitrary-oriented text detection in natural scene images.
no code implementations • 1 Feb 2017 • Li Wang, Yao Lu, Hong Wang, Yingbin Zheng, Hao Ye, xiangyang xue
We perform fast vehicle detection from traffic surveillance cameras.
1 code implementation • 22 Jun 2016 • Xingyi Zhou, Qingfu Wan, Wei zhang, xiangyang xue, Yichen Wei
For the first time, we show that embedding such a non-linear generative process in deep learning is feasible for hand pose estimation.
no code implementations • 8 Dec 2015 • Jie Shao, Dequan Wang, xiangyang xue, Zheng Zhang
This paper proposes the problem of point-and-count as a test case to break the what-and-where deadlock.
no code implementations • ICCV 2015 • Dequan Wang, Zhiqiang Shen, Jie Shao, Wei zhang, xiangyang xue, Zheng Zhang
Fine-grained categorization, which aims to distinguish subordinate-level categories such as bird species or dog breeds, is an extremely challenging task.
no code implementations • 21 Sep 2015 • Zuxuan Wu, Yu-Gang Jiang, Xi Wang, Hao Ye, xiangyang xue, Jun Wang
A multi-stream framework is proposed to fully utilize the rich multimodal information in videos.
no code implementations • CVPR 2015 • Wei Zhang, Sheng Zeng, Dequan Wang, xiangyang xue
Image semantic segmentation is the task of partitioning image into several regions based on semantic concepts.
no code implementations • 8 Apr 2015 • Hao Ye, Zuxuan Wu, Rui-Wei Zhao, Xi Wang, Yu-Gang Jiang, xiangyang xue
In this paper, we conduct an in-depth study to investigate important implementation options that may affect the performance of deep nets on video classification.
1 code implementation • 7 Apr 2015 • Zuxuan Wu, Xi Wang, Yu-Gang Jiang, Hao Ye, xiangyang xue
In this paper, we propose a hybrid deep learning framework for video classification, which is able to model static spatial information, short-term motion, as well as long-term temporal clues in the videos.
no code implementations • 25 Feb 2015 • Yu-Gang Jiang, Zuxuan Wu, Jun Wang, xiangyang xue, Shih-Fu Chang
In this paper, we study the challenging problem of categorizing videos according to high-level semantics such as the existence of a particular human action or a complex event.
no code implementations • 24 Sep 2014 • Zhiqiang Shen, xiangyang xue
In these fields, the outputs of all layers of CNNs are usually considered as a high dimensional feature vector extracted from an input image and the correspondence between finer level feature vectors and concepts that the input image contains is all-important.
no code implementations • IEEE International Conference on Computer Vision 2011 • Xiangyang Xue, Wei zhang, Jie Zhang, Bin Wu, Jianping Fan, Yao Lu
The cross-level label coherence en-codes the consistency between the labels at the image leveland the labels at the region level.