Search Results for author: Huimin Ma

Found 25 papers, 4 papers with code

MotionVideoGAN: A Novel Video Generator Based on the Motion Space Learned from Image Pairs

1 code implementation6 Mar 2023 Jingyuan Zhu, Huimin Ma, Jiansheng Chen, Jian Yuan

We present MotionVideoGAN, a novel video generator synthesizing videos based on the motion space learned by pre-trained image pair generators.

Unconditional Video Generation

Gestalt-Guided Image Understanding for Few-Shot Learning

1 code implementation8 Feb 2023 Kun Song, Yuchen Wu, Jiansheng Chen, Tianyu Hu, Huimin Ma

Due to the scarcity of available data, deep learning does not perform well on few-shot learning tasks.

Few-Shot Learning

Few-shot Image Generation with Diffusion Models

no code implementations7 Nov 2022 Jingyuan Zhu, Huimin Ma, Jiansheng Chen, Jian Yuan

Then we fine-tune DDPMs pre-trained on large source domains to solve the overfitting problem when training data is limited.

Denoising Domain Adaptation +1

Few-shot Image Generation via Masked Discrimination

no code implementations27 Oct 2022 Jingyuan Zhu, Huimin Ma, Jiansheng Chen, Jian Yuan

It strengthens global image discrimination and guides adapted GANs to preserve more information learned from source domains for higher image quality.

Image Generation

Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems

1 code implementation NeurIPS 2021 Jiayu Chen, Yuanxin Zhang, Yuanfan Xu, Huimin Ma, Huazhong Yang, Jiaming Song, Yu Wang, Yi Wu

We motivate our paradigm through a variational perspective, where the learning objective can be decomposed into two terms: task learning on the current task distribution, and curriculum update to a new task distribution.

Multi-agent Reinforcement Learning

Video Frame Interpolation via Structure-Motion based Iterative Fusion

no code implementations11 May 2021 Xi Li, Meng Cao, Yingying Tang, Scott Johnston, Zhendong Hong, Huimin Ma, Jiulong Shan

Inspired by the observation that audiences have different visual preferences on foreground and background objects, we for the first time propose to use saliency masks in the evaluation processes of the task of video frame interpolation.

Optical Flow Estimation Video Frame Interpolation

Defending Against Universal Adversarial Patches by Clipping Feature Norms

no code implementations ICCV 2021 Cheng Yu, Jiansheng Chen, Youze Xue, Yuyang Liu, Weitao Wan, Jiayu Bao, Huimin Ma

Physical-world adversarial attacks based on universal adversarial patches have been proved to be able to mislead deep convolutional neural networks (CNNs), exposing the vulnerability of real-world visual classification systems based on CNNs.

Unsupervised segmentation via semantic-apparent feature fusion

no code implementations21 May 2020 Xi Li, Huimin Ma, Hongbing Ma, Yidong Wang

In order to solve this problem, the research proposes an unsupervised foreground segmentation method based on semantic-apparent feature fusion (SAFF).

Weakly-Supervised Semantic Segmentation by Iterative Affinity Learning

no code implementations19 Feb 2020 Xiang Wang, Sifei Liu, Huimin Ma, Ming-Hsuan Yang

In this paper, we propose an iterative algorithm to learn such pairwise relations, which consists of two branches, a unary segmentation network which learns the label probabilities for each pixel, and a pairwise affinity network which learns affinity matrix and refines the probability map generated from the unary network.

Weakly supervised Semantic Segmentation Weakly-Supervised Semantic Segmentation

Semantic Head Enhanced Pedestrian Detection in a Crowd

no code implementations27 Nov 2019 Ruiqi Lu, Huimin Ma

Pedestrian detection in the crowd is a challenging task because of intra-class occlusion.

Head Detection Pedestrian Detection

Occluded Pedestrian Detection with Visible IoU and Box Sign Predictor

no code implementations26 Nov 2019 Ruiqi Lu, Huimin Ma

Training a robust classifier and an accurate box regressor are difficult for occluded pedestrian detection.

Pedestrian Detection

WSOD with PSNet and Box Regression

no code implementations26 Nov 2019 Sheng Yi, Xi Li, Huimin Ma

To solve this problem, we added the box regression module to the weakly supervised object detection network and proposed a proposal scoring network (PSNet) to supervise it.

object-detection Pseudo Label +2

Pretrain Soft Q-Learning with Imperfect Demonstrations

no code implementations9 May 2019 Xiaoqin Zhang, Yunfei Li, Huimin Ma, Xiong Luo

Pretraining reinforcement learning methods with demonstrations has been an important concept in the study of reinforcement learning since a large amount of computing power is spent on online simulations with existing reinforcement learning algorithms.

Q-Learning reinforcement-learning +1

Driving maneuvers prediction based on cognition-driven and data-driven method

no code implementations8 May 2018 Dong Zhou, Huimin Ma, Yuhan Dong

To overcome this challenge, we propose a novel method that combines both the cognition-driven model and the data-driven model.

Pretraining Deep Actor-Critic Reinforcement Learning Algorithms With Expert Demonstrations

no code implementations31 Jan 2018 Xiaoqin Zhang, Huimin Ma

We apply our method to two of the typical actor-critic reinforcement learning algorithms, DDPG and ACER, and demonstrate with experiments that our method not only outperforms the RL algorithms without pretraining process, but also is more simulation efficient.

reinforcement-learning reinforcement Learning

Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection

no code implementations29 Aug 2016 Xiang Wang, Huimin Ma, Xiaozhi Chen, ShaoDi You

In this paper, we propose a novel edge preserving and multi-scale contextual neural network for salient object detection.

object-detection RGB Salient Object Detection +2

Improving Object Proposals With Multi-Thresholding Straddling Expansion

no code implementations CVPR 2015 Xiaozhi Chen, Huimin Ma, Xiang Wang, Zhichen Zhao

Based on the characteristics of superpixel tightness distribution, we propose an effective method, namely multi-thresholding straddling expansion (MTSE) to reduce localization bias via fast diversification.

object-detection Object Detection

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