Search Results for author: Yaowu Chen

Found 20 papers, 3 papers with code

ESOD: Efficient Small Object Detection on High-Resolution Images

no code implementations23 Jul 2024 Kai Liu, Zhihang Fu, Sheng Jin, Ze Chen, Fan Zhou, Rongxin Jiang, Yaowu Chen, Jieping Ye

The resulting Efficient Small Object Detection (ESOD) approach is a generic framework, which can be applied to both CNN- and ViT-based detectors to save the computation and GPU memory costs.

Object object-detection +1

Rethinking Out-of-Distribution Detection on Imbalanced Data Distribution

no code implementations23 Jul 2024 Kai Liu, Zhihang Fu, Sheng Jin, Chao Chen, Ze Chen, Rongxin Jiang, Fan Zhou, Yaowu Chen, Jieping Ye

Detecting and rejecting unknown out-of-distribution (OOD) samples is critical for deployed neural networks to void unreliable predictions.

Out-of-Distribution Detection

Educating LLMs like Human Students: Structure-aware Injection of Domain Knowledge

no code implementations23 Jul 2024 Kai Liu, Ze Chen, Zhihang Fu, Rongxin Jiang, Fan Zhou, Yaowu Chen, Yue Wu, Jieping Ye

This paper presents a pioneering methodology, termed StructTuning, to efficiently transform foundation Large Language Models (LLMs) into domain specialists.

Question Answering

URRL-IMVC: Unified and Robust Representation Learning for Incomplete Multi-View Clustering

no code implementations12 Jul 2024 Ge Teng, Ting Mao, Chen Shen, Xiang Tian, Xuesong Liu, Yaowu Chen, Jieping Ye

To address these limitations, we propose a novel Unified and Robust Representation Learning for Incomplete Multi-View Clustering (URRL-IMVC).

Clustering Contrastive Learning +4

Self-Learning Symmetric Multi-view Probabilistic Clustering

no code implementations12 May 2023 Junjie Liu, Junlong Liu, Rongxin Jiang, Yaowu Chen, Chen Shen, Jieping Ye

Then, SLS-MPC proposes a novel self-learning probability function without any prior knowledge and hyper-parameters to learn each view's individual distribution.

Clustering Incomplete multi-view clustering +1

Information-containing Adversarial Perturbation for Combating Facial Manipulation Systems

no code implementations21 Mar 2023 Yao Zhu, Yuefeng Chen, Xiaodan Li, Rong Zhang, Xiang Tian, Bolun Zheng, Yaowu Chen

We use an encoder to map a facial image and its identity message to a cross-model adversarial example which can disrupt multiple facial manipulation systems to achieve initiative protection.

Fake Image Detection

Rethinking Out-of-Distribution Detection From a Human-Centric Perspective

no code implementations30 Nov 2022 Yao Zhu, Yuefeng Chen, Xiaodan Li, Rong Zhang, Hui Xue, Xiang Tian, Rongxin Jiang, Bolun Zheng, Yaowu Chen

Additionally, our experiments demonstrate that model selection is non-trivial for OOD detection and should be considered as an integral of the proposed method, which differs from the claim in existing works that proposed methods are universal across different models.

Model Selection Out-of-Distribution Detection +1

Boosting Out-of-distribution Detection with Typical Features

no code implementations9 Oct 2022 Yao Zhu, Yuefeng Chen, Chuanlong Xie, Xiaodan Li, Rong Zhang, Hui Xue, Xiang Tian, Bolun Zheng, Yaowu Chen

Out-of-distribution (OOD) detection is a critical task for ensuring the reliability and safety of deep neural networks in real-world scenarios.

Out-of-Distribution Detection

Towards Understanding and Boosting Adversarial Transferability from a Distribution Perspective

2 code implementations9 Oct 2022 Yao Zhu, Yuefeng Chen, Xiaodan Li, Kejiang Chen, Yuan He, Xiang Tian, Bolun Zheng, Yaowu Chen, Qingming Huang

We conduct comprehensive transferable attacks against multiple DNNs to demonstrate the effectiveness of the proposed method.

Spatial Likelihood Voting with Self-Knowledge Distillation for Weakly Supervised Object Detection

no code implementations14 Apr 2022 Ze Chen, Zhihang Fu, Jianqiang Huang, Mingyuan Tao, Rongxin Jiang, Xiang Tian, Yaowu Chen, Xian-Sheng Hua

The likelihood maps generated by the SLV module are used to supervise the feature learning of the backbone network, encouraging the network to attend to wider and more diverse areas of the image.

Multiple Instance Learning object-detection +3

Dynamic Supervisor for Cross-dataset Object Detection

no code implementations1 Apr 2022 Ze Chen, Zhihang Fu, Jianqiang Huang, Mingyuan Tao, Shengyu Li, Rongxin Jiang, Xiang Tian, Yaowu Chen, Xian-Sheng Hua

The application of cross-dataset training in object detection tasks is complicated because the inconsistency in the category range across datasets transforms fully supervised learning into semi-supervised learning.

Object object-detection +1

MPC: Multi-View Probabilistic Clustering

no code implementations CVPR 2022 Junjie Liu, Junlong Liu, Shaotian Yan, Rongxin Jiang, Xiang Tian, Boxuan Gu, Yaowu Chen, Chen Shen, Jianqiang Huang

Despite the promising progress having been made, the two challenges of multi-view clustering (MVC) are still waiting for better solutions: i) Most existing methods are either not qualified or require additional steps for incomplete multi-view clustering and ii) noise or outliers might significantly degrade the overall clustering performance.

Clustering Incomplete multi-view clustering

DGL-GAN: Discriminator Guided Learning for GAN Compression

1 code implementation13 Dec 2021 Yuesong Tian, Li Shen, Xiang Tian, DaCheng Tao, Zhifeng Li, Wei Liu, Yaowu Chen

Moreover, DGL-GAN is also effective in boosting the performance of original uncompressed GANs.

PCPL: Predicate-Correlation Perception Learning for Unbiased Scene Graph Generation

1 code implementation2 Sep 2020 Shaotian Yan, Chen Shen, Zhongming Jin, Jianqiang Huang, Rongxin Jiang, Yaowu Chen, Xian-Sheng Hua

Today, scene graph generation(SGG) task is largely limited in realistic scenarios, mainly due to the extremely long-tailed bias of predicate annotation distribution.

Graph Generation Unbiased Scene Graph Generation

SLV: Spatial Likelihood Voting for Weakly Supervised Object Detection

no code implementations CVPR 2020 Ze Chen, Zhihang Fu, Rongxin Jiang, Yaowu Chen, Xian-Sheng Hua

In this paper, we propose a spatial likelihood voting (SLV) module to converge the proposal localizing process without any bounding box annotations.

General Classification Multiple Instance Learning +4

Sharp Attention Network via Adaptive Sampling for Person Re-identification

no code implementations7 May 2018 Chen Shen, Guo-Jun Qi, Rongxin Jiang, Zhongming Jin, Hongwei Yong, Yaowu Chen, Xian-Sheng Hua

In this paper, we present novel sharp attention networks by adaptively sampling feature maps from convolutional neural networks (CNNs) for person re-identification (re-ID) problem.

Person Re-Identification

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