Search Results for author: Deyin Liu

Found 13 papers, 3 papers with code

A Deep Semantic Segmentation Network with Semantic and Contextual Refinements

no code implementations11 Dec 2024 Zhiyan Wang, Deyin Liu, Lin Yuanbo Wu, Song Wang, Xin Guo, Lin Qi

Additionally, this paper extends these modules to a lightweight segmentation network, achieving an mIoU of 82. 5% on the Cityscapes validation set with only 137. 9 GFLOPs.

Segmentation Semantic Segmentation

UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation

no code implementations13 Nov 2024 ChengYuan Zhang, Yilin Zhang, Lei Zhu, Deyin Liu, Lin Wu, Bo Li, Shichao Zhang, Mohammed Bennamoun, Farid Boussaid

This paper introduces a novel framework for unified incremental few-shot object detection (iFSOD) and instance segmentation (iFSIS) using the Transformer architecture.

Decoder Few-Shot Object Detection +5

Blended Latent Diffusion under Attention Control for Real-World Video Editing

no code implementations5 Sep 2024 Deyin Liu, Lin Yuanbo Wu, Xianghua Xie

First, although existing methods attempt to focus on local area editing by a pre-defined mask, the preservation of the outside-area background is non-ideal due to the spatially entire generation of each frame.

Text-to-Image Generation Video Editing

Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization

no code implementations3 Jul 2024 Hanxi Li, Jingqi Wu, Lin Yuanbo Wu, Hao Chen, Deyin Liu, Chunhua Shen

By fine-tuning the ADClick-Seg model using the weak labels inferred by ADClick, we establish the state-of-the-art performances in supervised AD tasks (AP $= 86. 4\%$ on MVTec AD and AP $= 78. 4\%$, PRO $= 98. 6\%$ on KSDD2).

Ranked #3 on Supervised Anomaly Detection on MVTec AD (using extra training data)

Image Segmentation Semantic Segmentation +1

A Novel Approach to Industrial Defect Generation through Blended Latent Diffusion Model with Online Adaptation

1 code implementation29 Feb 2024 Hanxi Li, Zhengxun Zhang, Hao Chen, Lin Wu, Bo Li, Deyin Liu, Mingwen Wang

Effectively addressing the challenge of industrial Anomaly Detection (AD) necessitates an ample supply of defective samples, a constraint often hindered by their scarcity in industrial contexts.

Anomaly Detection Decoder +1

LipFormer: Learning to Lipread Unseen Speakers based on Visual-Landmark Transformers

no code implementations4 Feb 2023 Feng Xue, Yu Li, Deyin Liu, Yincen Xie, Lin Wu, Richang Hong

However, generalizing these methods to unseen speakers incurs catastrophic performance degradation due to the limited number of speakers in training bank and the evident visual variations caused by the shape/color of lips for different speakers.

Lipreading Sentence

Asymmetric Cross-Scale Alignment for Text-Based Person Search

1 code implementation26 Nov 2022 Zhong Ji, Junhua Hu, Deyin Liu, Lin Yuanbo Wu, Ye Zhao

To implement this task, one needs to extract multi-scale features from both image and text domains, and then perform the cross-modal alignment.

cross-modal alignment Person Search +3

T-Person-GAN: Text-to-Person Image Generation with Identity-Consistency and Manifold Mix-Up

1 code implementation18 Aug 2022 Deyin Liu, Lin Yuanbo Wu, Bo Li, ZongYuan Ge

Our architecture is orthogonal to StackGAN++ , and focuses on person image generation, with all of them together to enrich the spectrum of GANs for the image generation task.

Text to Image Generation Text-to-Image Generation

Jacobian Norm with Selective Input Gradient Regularization for Improved and Interpretable Adversarial Defense

no code implementations9 Jul 2022 Deyin Liu, Lin Wu, Haifeng Zhao, Farid Boussaid, Mohammed Bennamoun, Xianghua Xie

Moreover, adversarially training a defense model in general cannot produce interpretable predictions towards the inputs with perturbations, whilst a highly interpretable robust model is required by different domain experts to understand the behaviour of a DNN.

Adversarial Defense

Pseudo-Pair based Self-Similarity Learning for Unsupervised Person Re-identification

no code implementations9 Jul 2022 Lin Wu, Deyin Liu, Wenying Zhang, Dapeng Chen, ZongYuan Ge, Farid Boussaid, Mohammed Bennamoun, Jialie Shen

In this paper, we present a pseudo-pair based self-similarity learning approach for unsupervised person re-ID without human annotations.

Unsupervised Person Re-Identification

HierTrain: Fast Hierarchical Edge AI Learning with Hybrid Parallelism in Mobile-Edge-Cloud Computing

no code implementations22 Mar 2020 Deyin Liu, Xu Chen, Zhi Zhou, Qing Ling

We develop a novel \textit{hybrid parallelism} method, which is the key to HierTrain, to adaptively assign the DNN model layers and the data samples across the three levels of edge device, edge server and cloud center.

Cloud Computing Scheduling

Medi-Care AI: Predicting Medications From Billing Codes via Robust Recurrent Neural Networks

no code implementations14 Nov 2019 Deyin Liu, Lin Wu, Xue Li

In this paper, we present an effective deep prediction framework based on robust recurrent neural networks (RNNs) to predict the likely therapeutic classes of medications a patient is taking, given a sequence of diagnostic billing codes in their record.

Diagnostic Missing Values

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