no code implementations • CVPR 2018 • Qi Cai, Yingwei Pan, Ting Yao, Chenggang Yan, Tao Mei
In this paper, we introduce the new ideas of augmenting Convolutional Neural Networks (CNNs) with Memory and learning to learn the network parameters for the unlabelled images on the fly in one-shot learning.
no code implementations • 13 Oct 2018 • Qi Cai, Yuanxin Wu, Lilian Zhang, Peike Zhang
The PPO constraints are simplified and formulated in the form of inequalities to directly identify the right pose solution with no need of 3D reconstruction and the 3D reconstruction can be analytically achieved from the identified right pose.
no code implementations • 11 Jan 2019 • Qi Cai, Mingyi Hong, Yongxin Chen, Zhaoran Wang
We study the global convergence of generative adversarial imitation learning for linear quadratic regulators, which is posed as minimax optimization.
no code implementations • 9 May 2019 • Qi Cai, Tsung-Ching Lin, Yuanxin Wu, Wenxian Yu, Trieu-Kien Truong
A general and fast method is conceived for computing the cyclic convolution of n points, where n is a prime number.
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 • 20 Jun 2019 • Fuchen Long, Qi Cai, Zhaofan Qiu, Zhijian Hou, Yingwei Pan, Ting Yao, Chong-Wah Ngo
This notebook paper presents an overview and comparative analysis of our system designed for activity detection in extended videos (ActEV-PC) in ActivityNet Challenge 2019.
no code implementations • 25 Jun 2019 • Boyi Liu, Qi Cai, Zhuoran Yang, Zhaoran Wang
Proximal policy optimization and trust region policy optimization (PPO and TRPO) with actor and critic parametrized by neural networks achieve significant empirical success in deep reinforcement learning.
no code implementations • ICLR 2020 • Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran Wang
In detail, we prove that neural natural policy gradient converges to a globally optimal policy at a sublinear rate.
no code implementations • NeurIPS 2019 • Boyi Liu, Qi Cai, Zhuoran Yang, Zhaoran Wang
Proximal policy optimization and trust region policy optimization (PPO and TRPO) with actor and critic parametrized by neural networks achieve significant empirical success in deep reinforcement learning.
no code implementations • NeurIPS 2019 • Qi Cai, Zhuoran Yang, Jason D. Lee, Zhaoran Wang
Temporal-difference learning (TD), coupled with neural networks, is among the most fundamental building blocks of deep reinforcement learning.
no code implementations • ICML 2020 • Qi Cai, Zhuoran Yang, Chi Jin, Zhaoran Wang
While policy-based reinforcement learning (RL) achieves tremendous successes in practice, it is significantly less understood in theory, especially compared with value-based RL.
no code implementations • 8 Mar 2020 • Yufeng Zhang, Qi Cai, Zhuoran Yang, Zhaoran Wang
Generative adversarial imitation learning (GAIL) demonstrates tremendous success in practice, especially when combined with neural networks.
no code implementations • 8 Jun 2020 • Yufeng Zhang, Qi Cai, Zhuoran Yang, Yongxin Chen, Zhaoran Wang
We aim to answer the following questions: When the function approximator is a neural network, how does the associated feature representation evolve?
no code implementations • ICML 2020 • Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran Wang
Model-agnostic meta-learning (MAML) formulates meta-learning as a bilevel optimization problem, where the inner level solves each subtask based on a shared prior, while the outer level searches for the optimal shared prior by optimizing its aggregated performance over all the subtasks.
no code implementations • ICML 2020 • Yufeng Zhang, Qi Cai, Zhuoran Yang, Zhaoran Wang
Generative adversarial imitation learning (GAIL) demonstrates tremendous success in practice, especially when combined with neural networks.
no code implementations • 1 Jan 2021 • Qi Cai, Zhuoran Yang, Csaba Szepesvari, Zhaoran Wang
Although policy optimization with neural networks has a track record of achieving state-of-the-art results in reinforcement learning on various domains, the theoretical understanding of the computational and sample efficiency of policy optimization remains restricted to linear function approximations with finite-dimensional feature representations, which hinders the design of principled, effective, and efficient algorithms.
no code implementations • 11 Oct 2020 • Shengjie Li, Qi Cai, Yuanxin Wu
Identifying feature correspondence between two images is a fundamental procedure in three-dimensional computer vision.
no code implementations • NeurIPS 2020 • Yufeng Zhang, Qi Cai, Zhuoran Yang, Yongxin Chen, Zhaoran Wang
Temporal-difference and Q-learning play a key role in deep reinforcement learning, where they are empowered by expressive nonlinear function approximators such as neural networks.
no code implementations • 5 Aug 2021 • Yu Wang, Jingyang Lin, Qi Cai, Yingwei Pan, Ting Yao, Hongyang Chao, Tao Mei
In this paper, we construct a novel probabilistic graphical model that effectively incorporates the low rank promoting prior into the framework of contrastive learning, referred to as LORAC.
no code implementations • NeurIPS 2021 • Boyi Liu, Qi Cai, Zhuoran Yang, Zhaoran Wang
Despite the tremendous success of reinforcement learning (RL) with function approximation, efficient exploration remains a significant challenge, both practically and theoretically.
no code implementations • 20 Apr 2022 • Qi Cai, Zhuoran Yang, Zhaoran Wang
The sample efficiency of OP-TENET is enabled by a sequence of ingredients: (i) a Bellman operator with finite memory, which represents the value function in a recursive manner, (ii) the identification and estimation of such an operator via an adversarial integral equation, which features a smoothed discriminator tailored to the linear structure, and (iii) the exploration of the observation and state spaces via optimism, which is based on quantifying the uncertainty in the adversarial integral equation.
no code implementations • 26 May 2022 • Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran Wang
For a class of POMDPs with a low-rank structure in the transition kernel, ETC attains an $O(1/\epsilon^2)$ sample complexity that scales polynomially with the horizon and the intrinsic dimension (that is, the rank).
no code implementations • 30 Dec 2022 • Yufeng Zhang, Boyi Liu, Qi Cai, Lingxiao Wang, Zhaoran Wang
In particular, such a representation instantiates the posterior distribution of the latent variable given input tokens, which plays a central role in predicting output labels and solving downstream tasks.
no code implementations • ICCV 2023 • Qi Cai, Yingwei Pan, Ting Yao, Chong-Wah Ngo, Tao Mei
Recent progress on multi-modal 3D object detection has featured BEV (Bird-Eye-View) based fusion, which effectively unifies both LiDAR point clouds and camera images in a shared BEV space.
no code implementations • 25 Dec 2023 • Yian Zhu, Ziye Jia, Qihui Wu, Chao Dong, Zirui Zhuang, Huiling Hu, Qi Cai
Therefore, we employ the ADS-B for UAV trajectory tracking in this work.
no code implementations • 24 Jan 2024 • Qi Cai, Xinrui Li, Yuanxin Wu
How to efficiently and accurately handle image matching outliers is a critical issue in two-view relative estimation.
no code implementations • 24 Mar 2024 • Tianrui Liu, Qi Cai, Changxin Xu, Bo Hong, Jize Xiong, Yuxin Qiao, Tsungwei Yang
Image captioning strives to generate pertinent captions for specified images, situating itself at the crossroads of Computer Vision (CV) and Natural Language Processing (NLP).
no code implementations • 24 Mar 2024 • Tianrui Liu, Qi Cai, Changxin Xu, Bo Hong, Fanghao Ni, Yuxin Qiao, Tsungwei Yang
In this paper, we propose a new detection model, that jointly learns both the representations of user correlation and information propagation to detect rumors on social media.
no code implementations • 26 Mar 2024 • Yurui Qian, Qi Cai, Yingwei Pan, Yehao Li, Ting Yao, Qibin Sun, Tao Mei
Diffusion models have recently brought a powerful revolution in image generation.
1 code implementation • NeurIPS 2019 • Qi Cai, Zhuoran Yang, Jason D. Lee, Zhaoran Wang
Temporal-difference learning (TD), coupled with neural networks, is among the most fundamental building blocks of deep reinforcement learning.
1 code implementation • 15 Sep 2020 • Dongrui Liu, Chuanchuan Chen, Changqing Xu, Qi Cai, Lei Chu, Fei Wen, Robert Caiming Qiu
We prove that CAT is a rotation and translation-invariant transformation based on the theoretical analysis.
1 code implementation • 26 Sep 2022 • Jingyang Lin, Yu Wang, Qi Cai, Yingwei Pan, Ting Yao, Hongyang Chao, Tao Mei
Existing works attempt to solve the problem by explicitly imposing uncertainty on classifiers when OOD inputs are exposed to the classifier during training.
1 code implementation • 13 Jun 2022 • Yingwei Pan, Yehao Li, Yiheng Zhang, Qi Cai, Fuchen Long, Zhaofan Qiu, Ting Yao, Tao Mei
This paper presents an overview and comparative analysis of our systems designed for the following two tracks in SAPIEN ManiSkill Challenge 2021: No Interaction Track: The No Interaction track targets for learning policies from pre-collected demonstration trajectories.
1 code implementation • 15 Nov 2022 • Qi Cai, Yingwei Pan, Ting Yao, Tao Mei
Recent progress on 2D object detection has featured Cascade RCNN, which capitalizes on a sequence of cascade detectors to progressively improve proposal quality, towards high-quality object detection.
1 code implementation • NeurIPS 2020 • Qi Cai, Yu Wang, Yingwei Pan, Ting Yao, Tao Mei
This paper explores useful modifications of the recent development in contrastive learning via novel probabilistic modeling.
1 code implementation • CVPR 2019 • Qi Cai, Yingwei Pan, Chong-Wah Ngo, Xinmei Tian, Ling-Yu Duan, Ting Yao
The whole architecture is then optimized with three consistency regularizations: 1) region-level consistency to align the region-level predictions between teacher and student, 2) inter-graph consistency for matching the graph structures between teacher and student, and 3) intra-graph consistency to enhance the similarity between regions of same class within the graph of student.
2 code implementations • 8 Oct 2019 • Yingwei Pan, Yehao Li, Qi Cai, Yang Chen, Ting Yao
Semi-Supervised Domain Adaptation: For this task, we adopt a standard self-learning framework to construct a classifier based on the labeled source and target data, and generate the pseudo labels for unlabeled target data.
1 code implementation • CVPR 2020 • Qi Cai, Yingwei Pan, Yu Wang, Jingen Liu, Ting Yao, Tao Mei
To this end, we devise a general loss function to cover most region-based object detectors with various sampling strategies, and then based on it we propose a unified sample weighting network to predict a sample's task weights.
1 code implementation • 2 Mar 2021 • Qi Cai, Lilian Zhang, Yuanxin Wu, Wenxian Yu, Dewen Hu
Visual navigation and three-dimensional (3D) scene reconstruction are essential for robotics to interact with the surrounding environment.