Search Results for author: Zeng-Guang Hou

Found 12 papers, 6 papers with code

DOMAIN: MilDly COnservative Model-BAsed OfflINe Reinforcement Learning

no code implementations16 Sep 2023 Xiao-Yin Liu, Xiao-Hu Zhou, Xiao-Liang Xie, Shi-Qi Liu, Zhen-Qiu Feng, Hao Li, Mei-Jiang Gui, Tian-Yu Xiang, De-Xing Huang, Zeng-Guang Hou

However, uncertainty estimation is unreliable and leads to poor performance in certain scenarios, and the previous methods ignore differences between the model data, which brings great conservatism.

D4RL Model-based Reinforcement Learning +3

Faster Person Re-Identification

1 code implementation ECCV 2020 Guan'an Wang, Shaogang Gong, Jian Cheng, Zeng-Guang Hou

In this work, we introduce a new solution for fast ReID by formulating a novel Coarse-to-Fine (CtF) hashing code search strategy, which complementarily uses short and long codes, achieving both faster speed and better accuracy.

Code Search Person Re-Identification +1

Cross-Modality Paired-Images Generation for RGB-Infrared Person Re-Identification

2 code implementations10 Feb 2020 Guan-An Wang, Tianzhu Zhang. Yang Yang, Jian Cheng, Jianlong Chang, Xu Liang, Zeng-Guang Hou

Second, given cross-modality unpaired-images of a person, our method can generate cross-modality paired images from exchanged images.

Person Re-Identification

BARNet: Bilinear Attention Network with Adaptive Receptive Fields for Surgical Instrument Segmentation

no code implementations20 Jan 2020 Zhen-Liang Ni, Gui-Bin Bian, Guan-An Wang, Xiao-Hu Zhou, Zeng-Guang Hou, Xiao-Liang Xie, Zhen Li, Yu-Han Wang

For the scale variation, our adaptive receptive field module aggregates multi-scale features and automatically fuses them with different weights.

Semantic Segmentation

Attention-Guided Lightweight Network for Real-Time Segmentation of Robotic Surgical Instruments

1 code implementation24 Oct 2019 Zhen-Liang Ni, Gui-Bin Bian, Zeng-Guang Hou, Xiao-Hu Zhou, Xiao-Liang Xie, Zhen Li

LWANet adopts encoder-decoder architecture, where the encoder is the lightweight network MobileNetV2, and the decoder consists of depthwise separable convolution, attention fusion block, and transposed convolution.

Semi-Supervised Generative Adversarial Hashing for Image Retrieval

no code implementations ECCV 2018 Guan'an Wang, Qinghao Hu, Jian Cheng, Zeng-Guang Hou

Secondly, we design novel structure of the generative model and the discriminative model to learn the distribution of triplet-wise information in a semi-supervised way.

Image Retrieval Retrieval +2

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