Search Results for author: Mengxi Jia

Found 7 papers, 1 papers with code

EraseAnything: Enabling Concept Erasure in Rectified Flow Transformers

no code implementations29 Dec 2024 Daiheng Gao, Shilin Lu, Shaw Walters, Wenbo Zhou, Jiaming Chu, Jie Zhang, Bang Zhang, Mengxi Jia, Jian Zhao, Zhaoxin Fan, Weiming Zhang

Removing unwanted concepts from large-scale text-to-image (T2I) diffusion models while maintaining their overall generative quality remains an open challenge.

Contrastive Learning

Reducing Events to Augment Log-based Anomaly Detection Models: An Empirical Study

no code implementations7 Sep 2024 Lingzhe Zhang, Tong Jia, Kangjin Wang, Mengxi Jia, Yang Yong, Ying Li

Experimental outcomes highlight LogCleaner's capability to reduce over 70% of log events in anomaly detection, accelerating the model's inference speed by approximately 300%, and universally improving the performance of models for anomaly detection.

Anomaly Detection

Population-Based Evolutionary Gaming for Unsupervised Person Re-identification

no code implementations8 Jun 2023 Yunpeng Zhai, Peixi Peng, Mengxi Jia, Shiyong Li, Weiqiang Chen, Xuesong Gao, Yonghong Tian

Extensive experiments demonstrate that (1) CRS approximately measures the performance of models without labeled samples; (2) and PEG produces new state-of-the-art accuracy for person re-identification, indicating the great potential of population-based network cooperative training for unsupervised learning.

Diversity Knowledge Distillation +1

Panoptic Compositional Feature Field for Editable Scene Rendering With Network-Inferred Labels via Metric Learning

no code implementations CVPR 2023 Xinhua Cheng, Yanmin Wu, Mengxi Jia, Qian Wang, Jian Zhang

In this work, we attempt to learn an object-compositional neural implicit representation for editable scene rendering by leveraging labels inferred from the off-the-shelf 2D panoptic segmentation networks instead of the ground truth annotations.

2D Panoptic Segmentation Metric Learning +2

Learning Disentangled Representation Implicitly via Transformer for Occluded Person Re-Identification

no code implementations6 Jul 2021 Mengxi Jia, Xinhua Cheng, Shijian Lu, Jian Zhang

To better eliminate interference from occlusions, we design a contrast feature learning technique (CFL) for better separation of occlusion features and discriminative ID features.

Decoder Occluded Person Re-Identification +1

Multiple Expert Brainstorming for Domain Adaptive Person Re-identification

2 code implementations ECCV 2020 Yunpeng Zhai, Qixiang Ye, Shijian Lu, Mengxi Jia, Rongrong Ji, Yonghong Tian

Often the best performing deep neural models are ensembles of multiple base-level networks, nevertheless, ensemble learning with respect to domain adaptive person re-ID remains unexplored.

Domain Adaptive Person Re-Identification Ensemble Learning +1

A Similarity Inference Metric for RGB-Infrared Cross-Modality Person Re-identification

no code implementations3 Jul 2020 Mengxi Jia, Yunpeng Zhai, Shijian Lu, Siwei Ma, Jian Zhang

RGB-Infrared (IR) cross-modality person re-identification (re-ID), which aims to search an IR image in RGB gallery or vice versa, is a challenging task due to the large discrepancy between IR and RGB modalities.

Cross-Modality Person Re-identification Person Re-Identification

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