Search Results for author: Jun Hou

Found 14 papers, 6 papers with code

StyleFlow For Content-Fixed Image to Image Translation

1 code implementation5 Jul 2022 Weichen Fan, Jinghuan Chen, Jiabin Ma, Jun Hou, Shuai Yi

We evaluate our model in several I2I translation benchmarks, and the results show that the proposed model has advantages over previous methods in both strongly constrained and normally constrained tasks.

Colorization Image-to-Image Translation +2

Pyramid Region-based Slot Attention Network for Temporal Action Proposal Generation

1 code implementation21 Jun 2022 Shuaicheng Li, Feng Zhang, Rui-Wei Zhao, Rui Feng, Kunlin Yang, Lingbo Liu, Jun Hou

Based on PRSlot modules, we present a novel Pyramid Region-based Slot Attention Network termed PRSA-Net to learn a unified visual representation with rich temporal and semantic context for better proposal generation.

Action Detection Temporal Action Proposal Generation

Better Teacher Better Student: Dynamic Prior Knowledge for Knowledge Distillation

no code implementations13 Jun 2022 Zengyu Qiu, Xinzhu Ma, Kunlin Yang, Chunya Liu, Jun Hou, Shuai Yi, Wanli Ouyang

More importantly, our DPK makes the performance of the student model is positively correlated with that of the teacher model, which means that we can further boost the accuracy of students by applying larger teachers.

Knowledge Distillation object-detection +1

GroupFormer: Group Activity Recognition with Clustered Spatial-Temporal Transformer

1 code implementation ICCV 2021 Shuaicheng Li, Qianggang Cao, Lingbo Liu, Kunlin Yang, Shinan Liu, Jun Hou, Shuai Yi

It captures spatial-temporal contextual information jointly to augment the individual and group representations effectively with a clustered spatial-temporal transformer.

Group Activity Recognition

Video Crowd Localization with Multi-focus Gaussian Neighborhood Attention and a Large-Scale Benchmark

1 code implementation19 Jul 2021 Haopeng Li, Lingbo Liu, Kunlin Yang, Shinan Liu, Junyu Gao, Bin Zhao, Rui Zhang, Jun Hou

Video crowd localization is a crucial yet challenging task, which aims to estimate exact locations of human heads in the given crowded videos.

Cloud-Based Dynamic Programming for an Electric City Bus Energy Management Considering Real-Time Passenger Load Prediction

no code implementations28 Oct 2020 Junzhe Shi, Bin Xu, Xingyu Zhou, Jun Hou

The proposed cloud-based dynamic programming and rule extraction framework with the passenger load prediction shows 4% and 11% fewer bus operating costs in off-peak and peak hours, respectively.

energy management Management

Learning Time Reduction Using Warm Start Methods for a Reinforcement Learning Based Supervisory Control in Hybrid Electric Vehicle Applications

no code implementations27 Oct 2020 Bin Xu, Jun Hou, Junzhe Shi, Huayi Li, Dhruvang Rathod, Zhe Wang, Zoran Filipi

This study aims to reduce the learning iterations of Q-learning in HEV application and improve fuel consumption in initial learning phases utilizing warm start methods.

Q-Learning

A Non-Intrusive Correction Algorithm for Classification Problems with Corrupted Data

no code implementations11 Feb 2020 Jun Hou, Tong Qin, Kailiang Wu, Dongbin Xiu

A novel correction algorithm is proposed for multi-class classification problems with corrupted training data.

Classification General Classification +1

GTC: Guided Training of CTC Towards Efficient and Accurate Scene Text Recognition

no code implementations4 Feb 2020 Wenyang Hu, Xiaocong Cai, Jun Hou, Shuai Yi, Zhiping Lin

Extensive experiments on standard benchmarks demonstrate that our end-to-end model achieves a new state-of-the-art for regular and irregular scene text recognition and needs 6 times shorter inference time than attentionbased methods.

Scene Text Recognition

Generation of Virtual Dual Energy Images from Standard Single-Shot Radiographs using Multi-scale and Conditional Adversarial Network

1 code implementation22 Oct 2018 Bo Zhou, Xunyu Lin, Brendan Eck, Jun Hou, David L. Wilson

Dual-energy (DE) chest radiographs provide greater diagnostic information than standard radiographs by separating the image into bone and soft tissue, revealing suspicious lesions which may otherwise be obstructed from view.

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