Search Results for author: Zhenye Gan

Found 13 papers, 9 papers with code

PSPU: Enhanced Positive and Unlabeled Learning by Leveraging Pseudo Supervision

no code implementations9 Jul 2024 Chengjie Wang, Chengming Xu, Zhenye Gan, Jianlong Hu, Wenbing Zhu, Lizhuag Ma

Positive and Unlabeled (PU) learning, a binary classification model trained with only positive and unlabeled data, generally suffers from overfitted risk estimation due to inconsistent data distributions.

Anomaly Detection Binary Classification

ADer: A Comprehensive Benchmark for Multi-class Visual Anomaly Detection

1 code implementation5 Jun 2024 Jiangning Zhang, Haoyang He, Zhenye Gan, Qingdong He, Yuxuan Cai, Zhucun Xue, Yabiao Wang, Chengjie Wang, Lei Xie, Yong liu

This paper addresses this issue by proposing a comprehensive visual anomaly detection benchmark, \textbf{\textit{ADer}}, which is a modular framework that is highly extensible for new methods.

Anomaly Detection Lesion Detection

Efficient Multimodal Large Language Models: A Survey

1 code implementation17 May 2024 Yizhang Jin, Jian Li, Yexin Liu, Tianjun Gu, Kai Wu, Zhengkai Jiang, Muyang He, Bo Zhao, Xin Tan, Zhenye Gan, Yabiao Wang, Chengjie Wang, Lizhuang Ma

In the past year, Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance in tasks such as visual question answering, visual understanding and reasoning.

Edge-computing Question Answering +1

DMAD: Dual Memory Bank for Real-World Anomaly Detection

no code implementations19 Mar 2024 Jianlong Hu, Xu Chen, Zhenye Gan, Jinlong Peng, Shengchuan Zhang, Jiangning Zhang, Yabiao Wang, Chengjie Wang, Liujuan Cao, Rongrong Ji

To address the challenge of real-world anomaly detection, we propose a new framework named Dual Memory bank enhanced representation learning for Anomaly Detection (DMAD).

Anomaly Detection Representation Learning

Transavs: End-To-End Audio-Visual Segmentation With Transformer

no code implementations12 May 2023 Yuhang Ling, Yuxi Li, Zhenye Gan, Jiangning Zhang, Mingmin Chi, Yabiao Wang

Generally AVS faces two key challenges: (1) Audio signals inherently exhibit a high degree of information density, as sounds produced by multiple objects are entangled within the same audio stream; (2) Objects of the same category tend to produce similar audio signals, making it difficult to distinguish between them and thus leading to unclear segmentation results.

Scene Understanding Segmentation +1

Calibrated Teacher for Sparsely Annotated Object Detection

1 code implementation14 Mar 2023 Haohan Wang, Liang Liu, Boshen Zhang, Jiangning Zhang, Wuhao Zhang, Zhenye Gan, Yabiao Wang, Chengjie Wang, Haoqian Wang

Recent works on sparsely annotated object detection alleviate this problem by generating pseudo labels for the missing annotations.

Object object-detection +2

Learning Distinctive Margin toward Active Domain Adaptation

1 code implementation CVPR 2022 Ming Xie, Yuxi Li, Yabiao Wang, Zekun Luo, Zhenye Gan, Zhongyi Sun, Mingmin Chi, Chengjie Wang, Pei Wang

Despite plenty of efforts focusing on improving the domain adaptation ability (DA) under unsupervised or few-shot semi-supervised settings, recently the solution of active learning started to attract more attention due to its suitability in transferring model in a more practical way with limited annotation resource on target data.

Active Learning Domain Adaptation

CFNet: Learning Correlation Functions for One-Stage Panoptic Segmentation

no code implementations13 Jan 2022 Yifeng Chen, Wenqing Chu, Fangfang Wang, Ying Tai, Ran Yi, Zhenye Gan, Liang Yao, Chengjie Wang, Xi Li

Recently, there is growing attention on one-stage panoptic segmentation methods which aim to segment instances and stuff jointly within a fully convolutional pipeline efficiently.

Instance Segmentation Panoptic Segmentation +1

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