Search Results for author: Pei Yan

Found 7 papers, 5 papers with code

MonoCD: Monocular 3D Object Detection with Complementary Depths

1 code implementation4 Apr 2024 Longfei Yan, Pei Yan, Shengzhou Xiong, Xuanyu Xiang, Yihua Tan

Monocular 3D object detection has attracted widespread attention due to its potential to accurately obtain object 3D localization from a single image at a low cost.

Depth Estimation Depth Prediction +3

Cross-Domain Few-Shot Segmentation via Iterative Support-Query Correspondence Mining

1 code implementation16 Jan 2024 Jiahao Nie, Yun Xing, Gongjie Zhang, Pei Yan, Aoran Xiao, Yap-Peng Tan, Alex C. Kot, Shijian Lu

Cross-Domain Few-Shot Segmentation (CD-FSS) poses the challenge of segmenting novel categories from a distinct domain using only limited exemplars.

Cross-Domain Few-Shot

Prompt Engineering-assisted Malware Dynamic Analysis Using GPT-4

1 code implementation13 Dec 2023 Pei Yan, Shunquan Tan, Miaohui Wang, Jiwu Huang

As a significant representation of dynamic malware behavior, the API (Application Programming Interface) sequence, comprised of consecutive API calls, has progressively become the dominant feature of dynamic analysis methods.

Few-Shot Learning Language Modelling +2

Unsupervised convolutional neural network fusion approach for change detection in remote sensing images

no code implementations7 Nov 2023 Weidong Yan, Pei Yan, Li Cao

Our model has three features: the entire training process is conducted in an unsupervised manner, the network architecture is shallow, and the objective function is sparse.

Change Detection

Repo2Vec: A Comprehensive Embedding Approach for Determining Repository Similarity

no code implementations11 Jul 2021 Md Omar Faruk Rokon, Pei Yan, Risul Islam, Michalis Faloutsos

Determiningrepository similarity is an essential building block in studying the dynamics and the evolution of such software ecosystems.

Unsupervised Learning Framework of Interest Point Via Properties Optimization

1 code implementation26 Jul 2019 Pei Yan, Yihua Tan, Yuan Xiao, Yuan Tai, Cai Wen

To maximize the objective efficiently, latent variable is introduced to represent the probability of that a point satisfies the required properties.

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