no code implementations • ECCV 2020 • Jiashu Zhu, Dong Li, Tiantian Han, Lu Tian, Yi Shan
In this work, we propose a novel scale-aware progressive training mechanism to address large scale variations across faces.
no code implementations • 23 Jan 2023 • Yuan Feng, Hyeran Jeon, Filip Blagojevic, Cyril Guyot, Qing Li, Dong Li
However, the transformer is computation-intensive, causing a long inference time.
no code implementations • 30 Dec 2022 • Hangyu Mao, Rui Zhao, Hao Chen, Jianye Hao, Yiqun Chen, Dong Li, Junge Zhang, Zhen Xiao
Recent methods combine the Transformer with these modules for better performance.
no code implementations • 20 Dec 2022 • Dong Li, Yelong Shen, Ruoming Jin, Yi Mao, Kuan Wang, Weizhu Chen
Pre-trained language models have achieved promising success in code retrieval tasks, where a natural language documentation query is given to find the most relevant existing code snippet.
no code implementations • 28 Nov 2022 • Dong Li, Ruoming Jin, Zhenming Liu, Bin Ren, Jing Gao, Zhi Liu
Since Rendle and Krichene argued that commonly used sampling-based evaluation metrics are ``inconsistent'' with respect to the global metrics (even in expectation), there have been a few studies on the sampling-based recommender system evaluation.
no code implementations • 28 Nov 2022 • Chen Chen, Hongyao Tang, Yi Ma, Chao Wang, Qianli Shen, Dong Li, Jianye Hao
The key idea of SA-PP is leveraging discounted stationary state distribution ratios between the learning policy and the offline dataset to modulate the degree of behavior regularization in a state-wise manner, so that pessimism can be implemented in a more appropriate way.
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
no code implementations • 17 Oct 2022 • Yiqun Chen, Hangyu Mao, Tianle Zhang, Shiguang Wu, Bin Zhang, Jianye Hao, Dong Li, Bin Wang, Hongxing Chang
Centralized Training with Decentralized Execution (CTDE) has been a very popular paradigm for multi-agent reinforcement learning.
no code implementations • 15 Oct 2022 • Kaiyue Lu, Zexiang Liu, Jianyuan Wang, Weixuan Sun, Zhen Qin, Dong Li, Xuyang Shen, Hui Deng, Xiaodong Han, Yuchao Dai, Yiran Zhong
Therefore, we propose a feature fixation module to reweight the feature importance of the query and key before computing linear attention.
no code implementations • 21 Sep 2022 • Haozhi Wang, Qing Wang, Yunfeng Shao, Dong Li, Jianye Hao, Yinchuan Li
Modern meta-reinforcement learning (Meta-RL) methods are mainly developed based on model-agnostic meta-learning, which performs policy gradient steps across tasks to maximize policy performance.
1 code implementation • COLING 2022 • Pancheng Wang, Shasha Li, Kunyuan Pang, Liangliang He, Dong Li, Jintao Tang, Ting Wang
Multi-Document Scientific Summarization (MDSS) aims to produce coherent and concise summaries for clusters of topic-relevant scientific papers.
no code implementations • 3 Aug 2022 • Chang Meng, Ziqi Zhao, Wei Guo, Yingxue Zhang, Haolun Wu, Chen Gao, Dong Li, Xiu Li, Ruiming Tang
More specifically, we propose a novel Coarse-to-fine Knowledge-enhanced Multi-interest Learning (CKML) framework to learn shared and behavior-specific interests for different behaviors.
no code implementations • 28 Jul 2022 • Zexiang Liu, Dong Li, Kaiyue Lu, Zhen Qin, Weixuan Sun, Jiacheng Xu, Yiran Zhong
To address this issue, we propose a new framework to find optimal architectures for efficient Transformers with the neural architecture search (NAS) technique.
no code implementations • 23 Jul 2022 • Ji Liu, Dong Li, Zekun Li, Han Liu, Wenjing Ke, Lu Tian, Yi Shan
Sample assignment plays a prominent part in modern object detection approaches.
no code implementations • 21 Jul 2022 • Jianwei Zhang, Dong Li, Lituan Wang, Lei Zhang
To address the problem, an improved augmentation search strategy, named Augmented Density Matching, was proposed by randomly sampling policies from a prior distribution for training.
no code implementations • 5 Jul 2022 • Xianhua Yu, Dong Li
However, most of existing works on IRS focus on how to compute the phase shift for performance enhancement, and the problem on how to obtain the computed phase shift at the IRS side is generally neglected.
no code implementations • 30 May 2022 • Changmin Yu, David Mguni, Dong Li, Aivar Sootla, Jun Wang, Neil Burgess
Efficient reinforcement learning (RL) involves a trade-off between "exploitative" actions that maximise expected reward and "explorative'" ones that sample unvisited states.
no code implementations • 16 May 2022 • Chao Wang, Chen Chen, Dong Li, Bin Wang
Recently, reinforcement learning has been used to address logic synthesis by formulating the operator sequence optimization problem as a Markov decision process.
no code implementations • CVPR 2022 • Qinghang Hong, Fengming Liu, Dong Li, Ji Liu, Lu Tian, Yi Shan
Sparse R-CNN is a recent strong object detection baseline by set prediction on sparse, learnable proposal boxes and proposal features.
no code implementations • CVPR 2022 • Haowei Zhu, Wenjing Ke, Dong Li, Ji Liu, Lu Tian, Yi Shan
First, we propose global-local cross-attention (GLCA) to enhance the interactions between global images and local high-response regions, which can help reinforce the spatial-wise discriminative clues for recognition.
Ranked #4 on
Fine-Grained Image Classification
on CUB-200-2011
Fine-Grained Image Classification
Fine-Grained Visual Categorization
no code implementations • 3 May 2022 • Zhigang Yan, Dong Li, Zhichao Zhang, Jiguang He
local update) and that of aggregations (a. k. a.
no code implementations • 25 Mar 2022 • Li Ke, Meng Pan, Weigao Wen, Dong Li
Learning with few labeled data is a key challenge for visual recognition, as deep neural networks tend to overfit using a few samples only.
no code implementations • 24 Mar 2022 • Bowen Wang, Guibao Shen, Dong Li, Jianye Hao, Wulong Liu, Yu Huang, HongZhong Wu, Yibo Lin, Guangyong Chen, Pheng Ann Heng
Precise congestion prediction from a placement solution plays a crucial role in circuit placement.
no code implementations • 10 Mar 2022 • Xiaotian Hao, Hangyu Mao, Weixun Wang, Yaodong Yang, Dong Li, Yan Zheng, Zhen Wang, Jianye Hao
To break this curse, we propose a unified agent permutation framework that exploits the permutation invariance (PI) and permutation equivariance (PE) inductive biases to reduce the multiagent state space.
no code implementations • 7 Mar 2022 • Meng Xiao, Ziyue Qiao, Yanjie Fu, Hao Dong, Yi Du, Pengyang Wang, Dong Li, Yuanchun Zhou
After extracting the semantic and interdisciplinary knowledge, we design a level-wise prediction component to fuse the two types of knowledge representations and detect interdisciplinary topic paths for each proposal.
no code implementations • CVPR 2021 • Dong Li, Zhaofan Qiu, Yingwei Pan, Ting Yao, Houqiang Li, Tao Mei
For each action category, we execute online clustering to decompose the graph into sub-graphs on each scale through learning Gaussian Mixture Layer and select the discriminative sub-graphs as action prototypes for recognition.
no code implementations • 31 Dec 2021 • Xiang Li, Dong Li, Ruoming Jin, Gagan Agrawal, Rajiv Ramnath
Though other methods (particularly those based on Laplacian Smoothing) have reported better accuracy, a fundamental limitation of all the work is a lack of scalability.
no code implementations • 26 Dec 2021 • Claire Glanois, Xuening Feng, Zhaohui Jiang, Paul Weng, Matthieu Zimmer, Dong Li, Wulong Liu
We propose an efficient interpretable neuro-symbolic model to solve Inductive Logic Programming (ILP) problems.
no code implementations • 24 Dec 2021 • Claire Glanois, Paul Weng, Matthieu Zimmer, Dong Li, Tianpei Yang, Jianye Hao, Wulong Liu
To that aim, we distinguish interpretability (as a property of a model) and explainability (as a post-hoc operation, with the intervention of a proxy) and discuss them in the context of RL with an emphasis on the former notion.
1 code implementation • NeurIPS 2021 • Chenyang Wu, Guoyu Yang, Zongzhang Zhang, Yang Yu, Dong Li, Wulong Liu, Jianye Hao
A belief is a distribution of states representing state uncertainty.
1 code implementation • ICLR 2022 • Changmin Yu, Dong Li, Jianye Hao, Jun Wang, Neil Burgess
We propose learning via retracing, a novel self-supervised approach for learning the state representation (and the associated dynamics model) for reinforcement learning tasks.
no code implementations • 17 Nov 2021 • Hangyu Mao, Chao Wang, Xiaotian Hao, Yihuan Mao, Yiming Lu, Chengjie WU, Jianye Hao, Dong Li, Pingzhong Tang
The MineRL competition is designed for the development of reinforcement learning and imitation learning algorithms that can efficiently leverage human demonstrations to drastically reduce the number of environment interactions needed to solve the complex \emph{ObtainDiamond} task with sparse rewards.
no code implementations • 10 Nov 2021 • Amur Ghose, Vincent Zhang, Yingxue Zhang, Dong Li, Wulong Liu, Mark Coates
To address this limitation, we propose a framework that can directly learn embeddings for the given netlist to enhance the quality of our node features.
1 code implementation • 24 Oct 2021 • Xianhua Yu, Dong Li, Yongjun Xu, Ying-Chang Liang
To this end, it is crucial to adjust the phases of reflecting elements of the IRS, and most of the research works focus on how to optimize/quantize the phase for different optimization objectives.
1 code implementation • 8 Oct 2021 • Shiyu Huang, Bin Wang, Dong Li, Jianye Hao, Ting Chen, Jun Zhu
In this work, we propose a new algorithm for circuit routing, named Ranking Cost, which innovatively combines search-based methods (i. e., A* algorithm) and learning-based methods (i. e., Evolution Strategies) to form an efficient and trainable router.
no code implementations • 29 Sep 2021 • Hangyu Mao, Jianye Hao, Dong Li, Jun Wang, Weixun Wang, Xiaotian Hao, Bin Wang, Kun Shao, Zhen Xiao, Wulong Liu
In contrast, we formulate an \emph{explicit} credit assignment problem where each agent gives its suggestion about how to weight individual Q-values to explicitly maximize the joint Q-value, besides guaranteeing the Bellman optimality of the joint Q-value.
no code implementations • 29 Sep 2021 • Dong Li, Zhenming Liu, Ruoming Jin, Zhi Liu, Jing Gao, Bin Ren
Recently, a wide range of recommendation algorithms inspired by deep learning techniques have emerged as the performance leaders several standard recommendation benchmarks.
no code implementations • 29 Sep 2021 • Pengjie Gu, Mengchen Zhao, Chen Chen, Dong Li, Jianye Hao, Bo An
Offline reinforcement learning is a promising approach for practical applications since it does not require interactions with real-world environments.
1 code implementation • 24 Aug 2021 • Xidong Feng, Chen Chen, Dong Li, Mengchen Zhao, Jianye Hao, Jun Wang
Meta learning, especially gradient based one, can be adopted to tackle this problem by learning initial parameters of the model and thus allowing fast adaptation to a specific task from limited data examples.
no code implementations • ICCV 2021 • Takashi Isobe, Dong Li, Lu Tian, Weihua Chen, Yi Shan, Shengjin Wang
We observe that these proposed schemes are capable of facilitating the learning of discriminative feature representations.
no code implementations • 20 Jun 2021 • Dong Li, Ruoming Jin, Jing Gao, Zhi Liu
Recently, Rendle has warned that the use of sampling-based top-$k$ metrics might not suffice.
no code implementations • CVPR 2021 • Ji Liu, Dong Li, Rongzhang Zheng, Lu Tian, Yi Shan
To this end, we comprehensively investigate three types of ranking constraints, i. e., global ranking, class-specific ranking and IoU-guided ranking losses.
no code implementations • 13 Jun 2021 • Runshi Liu, Pengda Qin, Yuhong Li, Weigao Wen, Dong Li, Kefeng Deng, Qiang Wu
Typically, the risk can be identified by jointly considering product content (e. g., title and image) and seller behavior.
no code implementations • 1 Jun 2021 • Tianze Zhou, Fubiao Zhang, Kun Shao, Kai Li, Wenhan Huang, Jun Luo, Weixun Wang, Yaodong Yang, Hangyu Mao, Bin Wang, Dong Li, Wulong Liu, Jianye Hao
In addition, we use a novel agent network named Population Invariant agent with Transformer (PIT) to realize the coordination transfer in more varieties of scenarios.
no code implementations • 27 May 2021 • Ruoming Jin, Dong Li, Jing Gao, Zhi Liu, Li Chen, Yang Zhou
Through the derivation and analysis of the closed-form solutions for two basic regression and matrix factorization approaches, we found these two approaches are indeed inherently related but also diverge in how they "scale-down" the singular values of the original user-item interaction matrix.
no code implementations • 21 Mar 2021 • Li Wang, Dong Li, Han Liu, Jinzhang Peng, Lu Tian, Yi Shan
Our goal is to train a unified model for improving the performance in each dataset by leveraging information from all the datasets.
no code implementations • 15 Mar 2021 • Zhihao Ma, Yuzheng Zhuang, Paul Weng, Hankz Hankui Zhuo, Dong Li, Wulong Liu, Jianye Hao
To address this challenge and improve the transparency, we propose a Neural Symbolic Reinforcement Learning framework by introducing symbolic logic into DRL.
no code implementations • ICLR Workshop SSL-RL 2021 • Changmin Yu, Dong Li, Hangyu Mao, Jianye Hao, Neil Burgess
Representation learning is a popular approach for reinforcement learning (RL) tasks with partially observable Markov decision processes.
no code implementations • 2 Mar 2021 • Ruoming Jin, Dong Li, Benjamin Mudrak, Jing Gao, Zhi Liu
The proposed approaches either are rather uninformative (linking sampling to metric evaluation) or can only work on simple metrics, such as Recall/Precision (Krichene and Rendle 2020; Li et al. 2020).
no code implementations • 4 Feb 2021 • Dong Li, Song Sun, Yao Yao
After propagating through a random amplifying medium, a squeezed state commonly shows excess noise above the shot-noise level.
Quantum Physics
no code implementations • 29 Jan 2021 • Yuhu Miao, Dong Li, Ding Yuan, Chaowei Jiang, Abouazza Elmhamdi, Mingyu Zhao, Sergey Anfinogentov
Quasi-periodic fast propagating (QFP) waves are often excited by solar flares, and could be trapped in the coronal structure with low Alfv\'en speed, so they could be used as a diagnosing tool for both the flaring core and magnetic waveguide.
Solar and Stellar Astrophysics
3 code implementations • 18 Jan 2021 • Jie Ren, Samyam Rajbhandari, Reza Yazdani Aminabadi, Olatunji Ruwase, Shuangyan Yang, Minjia Zhang, Dong Li, Yuxiong He
By combining compute and memory efficiency with ease-of-use, ZeRO-Offload democratizes large-scale model training making it accessible to even data scientists with access to just a single GPU.
no code implementations • 1 Jan 2021 • Zhihao Ma, Yuzheng Zhuang, Paul Weng, Dong Li, Kun Shao, Wulong Liu, Hankz Hankui Zhuo, Jianye Hao
Recent progress in deep reinforcement learning (DRL) can be largely attributed to the use of neural networks.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
no code implementations • 1 Jan 2021 • Yizheng Hu, Kun Shao, Dong Li, Jianye Hao, Wulong Liu, Yaodong Yang, Jun Wang, Zhanxing Zhu
Therefore, to achieve robust CMARL, we introduce novel strategies to encourage agents to learn correlated equilibrium while maximally preserving the convenience of the decentralized execution.
no code implementations • 1 Jan 2021 • Yizhou Chen, Dong Li, Na Li, TONG LIANG, Shizhuo Zhang, Bryan Kian Hsiang Low
This paper presents a novel implicit process-based meta-learning (IPML) algorithm that, in contrast to existing works, explicitly represents each task as a continuous latent vector and models its probabilistic belief within the highly expressive IP framework.
no code implementations • 1 Jan 2021 • Xiangkun He, Jianye Hao, Dong Li, Bin Wang, Wulong Liu
Thirdly, the agent’s learning process is regarded as a black-box, and the comprehensive metric we proposed is computed after each episode of training, then a Bayesian optimization (BO) algorithm is adopted to guide the agent to evolve towards improving the quality of the approximated Pareto frontier.
no code implementations • 1 Jan 2021 • Shiyu Huang, Bin Wang, Dong Li, Jianye Hao, Jun Zhu, Ting Chen
In our method, we introduce a new set of variables called cost maps, which can help the A* router to find out proper paths to achieve the global object.
no code implementations • ICCV 2021 • Tiantian Han, Dong Li, Ji Liu, Lu Tian, Yi Shan
Such bin regularization (BR) mechanism encourages the weight distribution of each quantization bin to be sharp and approximate to a Dirac delta distribution ideally.
1 code implementation • 9 Dec 2020 • Haijun Liu, Yanxia Chai, Xiaoheng Tan, Dong Li, Xichuan Zhou
In this letter, we propose a conceptually simple and effective dual-granularity triplet loss for visible-thermal person re-identification (VT-ReID).
Ranked #2 on
Cross-Modal Person Re-Identification
on SYSU-MM01
1 code implementation • 6 Dec 2020 • Zehui Gong, Dong Li
It is a common practice to exploit pyramidal feature representation to tackle the problem of scale variation in object instances.
no code implementations • NeurIPS 2020 • Jie Ren, Minjia Zhang, Dong Li
The emergence of heterogeneous memory (HM) brings a solution to significantly increase memory capacity and break the above tradeoff: Using HM, billions of data points can be placed in the main memory on a single machine without using any data compression.
no code implementations • NeurIPS 2021 • Hongyao Tang, Zhaopeng Meng, Jianye Hao, Chen Chen, Daniel Graves, Dong Li, Changmin Yu, Hangyu Mao, Wulong Liu, Yaodong Yang, Wenyuan Tao, Li Wang
We study Policy-extended Value Function Approximator (PeVFA) in Reinforcement Learning (RL), which extends conventional value function approximator (VFA) to take as input not only the state (and action) but also an explicit policy representation.
1 code implementation • 14 Oct 2020 • Dong Li, Sitong Chen, Xudong Liu, YunDa Sun, Li Zhang
In this paper, we propose a balanced filter pruning method for both performance and pruning speed.
1 code implementation • 29 Sep 2020 • Haotian Fu, Hongyao Tang, Jianye Hao, Chen Chen, Xidong Feng, Dong Li, Wulong Liu
How to collect informative trajectories of which the corresponding context reflects the specification of tasks?
no code implementations • 28 Sep 2020 • Hongyao Tang, Zhaopeng Meng, Jianye Hao, Chen Chen, Daniel Graves, Dong Li, Wulong Liu, Yaodong Yang
The value function lies in the heart of Reinforcement Learning (RL), which defines the long-term evaluation of a policy in a given state.
no code implementations • 28 Sep 2020 • Tianpei Yang, Jianye Hao, Weixun Wang, Hongyao Tang, Zhaopeng Meng, Hangyu Mao, Dong Li, Wulong Liu, Yujing Hu, Yingfeng Chen, Changjie Fan
In many cases, each agent's experience is inconsistent with each other which causes the option-value estimation to oscillate and to become inaccurate.
no code implementations • 26 Aug 2020 • Wenqian Dong, Zhen Xie, Gokcen Kestor, Dong Li
In this paper, we develop a neural network approach to the problem of accelerating the current optimal power flow (AC-OPF) by generating an intelligent initial solution.
no code implementations • 26 Aug 2020 • Wenqian Dong, Jie Liu, Zhen Xie, Dong Li
Evaluating with 20, 480 input problems, we show that Smartfluidnet achieves 1. 46x and 590x speedup comparing with a state-of-the-art neural network model and the original fluid simulation respectively on an NVIDIA Titan X Pascal GPU, while providing better simulation quality than the state-of-the-art model.
no code implementations • 12 Apr 2020 • Zhi Liu, Yan Huang, Jing Gao, Li Chen, Dong Li
Similar product recommendation is one of the most common scenes in e-commerce.
no code implementations • 31 Mar 2020 • Dong Li, Ting Yao, Zhaofan Qiu, Houqiang Li, Tao Mei
It has been well recognized that modeling human-object or object-object relations would be helpful for detection task.
no code implementations • 17 Mar 2020 • Hao Yang, Dan Yan, Li Zhang, Dong Li, YunDa Sun, ShaoDi You, Stephen J. Maybank
It transmits the high-level semantic features to the low-level layers and flows temporal information stage by stage to progressively model global spatial-temporal features for action recognition; (3) The FGCN model provides early predictions.
Ranked #24 on
Skeleton Based Action Recognition
on NTU RGB+D 120
no code implementations • 3 Mar 2020 • Jie Liu, Jiawen Liu, Zhen Xie, Dong Li
How to accurately and efficiently label data on a mobile device is critical for the success of training machine learning models on mobile devices.
no code implementations • 3 Dec 2019 • Hangyu Mao, Wulong Liu, Jianye Hao, Jun Luo, Dong Li, Zhengchao Zhang, Jun Wang, Zhen Xiao
Social psychology and real experiences show that cognitive consistency plays an important role to keep human society in order: if people have a more consistent cognition about their environments, they are more likely to achieve better cooperation.
no code implementations • 8 Sep 2019 • Dong Li, Bo Ouyang, Duanpo Wu, Yaonan Wang
AGVs are driverless robotic vehicles that picks up and delivers materials.
3 code implementations • 22 Aug 2019 • Ao Li, Zhou Qin, Runshi Liu, Yiqun Yang, Dong Li
Furthermore, we deploy our system to process million-scale data daily at Xianyu.
2 code implementations • 26 Jul 2019 • Zhulin Zhang, Dong Li, Jinhua Wu, YunDa Sun, Li Zhang
Second, all baggage images are captured by specially-designed multi-view camera system to handle pose variation and occlusion, in order to obtain the 3D information of baggage surface as complete as possible.
no code implementations • 3 Jul 2019 • Dong Li, Lin Li
The Q&A community has become an important way for people to access knowledge and information from the Internet.
no code implementations • 14 Jun 2019 • Zhaofan Qiu, Dong Li, Yehao Li, Qi Cai, Yingwei Pan, Ting Yao
This notebook paper presents an overview and comparative analysis of our systems designed for the following three tasks in ActivityNet Challenge 2019: trimmed action recognition, dense-captioning events in videos, and spatio-temporal action localization.
no code implementations • 10 Jun 2019 • Jie Liu, Jiawen Liu, Wan Du, Dong Li
In this paper, we perform a variety of experiments on a representative mobile device (the NVIDIA TX2) to study the performance of training deep learning models.
no code implementations • 11 May 2019 • Dong Li, Qichao Zhang, Dongbin Zhao, Yuzheng Zhuang, Bin Wang, Wulong Liu, Rasul Tutunov, Jun Wang
To address the long-term memory issue, this paper proposes a graph attention memory (GAM) architecture consisting of memory construction module, graph attention module and control module.
1 code implementation • 18 Dec 2018 • Wenqian Dong, Anzheng Guolu, Dong Li
Machine learning, as a tool to learn and model complicated (non)linear relationships between input and output data sets, has shown preliminary success in some HPC problems.
1 code implementation • 22 Nov 2018 • Jian-Feng Cai, Dong Li, Jiaze Sun, Ke Wang
The key in our proof is that random projections embed stably the set of sparse vectors or a low-dimensional smooth manifold into a low-dimensional subspace.
1 code implementation • 30 Oct 2018 • Dong Li, Dongbin Zhao, Qichao Zhang, Yaran Chen
The control module which is based on reinforcement learning then makes a control decision based on these features.
no code implementations • 21 Oct 2018 • Jiawen Liu, Dong Li, Gokcen Kestor, Jeffrey Vetter
These frameworks employ a dataflow model where the NN training is modeled as a directed graph composed of a set of nodes.
no code implementations • ECCV 2018 • Dong Li, Zhaofan Qiu, Qi Dai, Ting Yao, Tao Mei
The RTP initializes action proposals of the start frame through a Region Proposal Network and then estimates the movements of proposals in next frame in a recurrent manner.
1 code implementation • 20 Jul 2018 • Haoyang Fan, Fan Zhu, Changchun Liu, Liangliang Zhang, Li Zhuang, Dong Li, Weicheng Zhu, Jiangtao Hu, Hongye Li, Qi Kong
In this manuscript, we introduce a real-time motion planning system based on the Baidu Apollo (open source) autonomous driving platform.
1 code implementation • 12 Oct 2017 • Dong Li, Jia-Bin Huang, Ya-Li Li, Shengjin Wang, Ming-Hsuan Yang
In classification adaptation, we transfer a pre-trained network to a multi-label classification task for recognizing the presence of a certain object in an image.
1 code implementation • 30 Aug 2017 • Hailiang Li, Kin-Man Lam, Dong Li
In the JMPF scheme, the original feature space is transformed into a compactly pre-clustered feature space, via a trained rotation matrix.
Ranked #48 on
Image Super-Resolution
on BSD100 - 4x upscaling
no code implementations • 5 Jun 2017 • Dong Li, Hsin-Ying Lee, Jia-Bin Huang, Shengjin Wang, Ming-Hsuan Yang
First, we exploit the discriminative constraints to capture the intra- and inter-class relationships of image embeddings.
no code implementations • CVPR 2016 • Dong Li, Jia-Bin Huang, Ya-Li Li, Shengjin Wang, Ming-Hsuan Yang
In this paper, we address this problem by progressive domain adaptation with two main steps: classification adaptation and detection adaptation.