Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection

1 code implementation • 19 Oct 2023 • Jiawen Zhu, Choubo Ding, Yu Tian, Guansong Pang

Open-set supervised anomaly detection (OSAD) - a recently emerging anomaly detection area - aims at utilizing a few samples of anomaly classes seen during training to detect unseen anomalies (i. e., samples from open-set anomaly classes), while effectively identifying the seen anomalies.

Supervised Anomaly Detection

DCPT: Darkness Clue-Prompted Tracking in Nighttime UAVs

1 code implementation • 19 Sep 2023 • Jiawen Zhu, Huayi Tang, Zhi-Qi Cheng, Jun-Yan He, Bin Luo, Shihao Qiu, Shengming Li, Huchuan Lu

To address this, we propose a novel architecture called Darkness Clue-Prompted Tracking (DCPT) that achieves robust UAV tracking at night by efficiently learning to generate darkness clue prompts.

Tracking Anything in High Quality

1 code implementation • 26 Jul 2023 • Jiawen Zhu, Zhenyu Chen, Zeqi Hao, Shijie Chang, Lu Zhang, Dong Wang, Huchuan Lu, Bin Luo, Jun-Yan He, Jin-Peng Lan, Hanyuan Chen, Chenyang Li

To further improve the quality of tracking masks, a pretrained MR model is employed to refine the tracking results.

Semantic Segmentation Semi-Supervised Video Object Segmentation +2

3rd Place Solution for PVUW2023 VSS Track: A Large Model for Semantic Segmentation on VSPW

1 code implementation • 4 Jun 2023 • Shijie Chang, Zeqi Hao, Ben Kang, Xiaoqi Zhao, Jiawen Zhu, Zhenyu Chen, Lihe Zhang, Lu Zhang, Huchuan Lu

In this paper, we introduce 3rd place solution for PVUW2023 VSS track.

Segmentation Semantic Segmentation +2

A Causal Inference Framework for Leveraging External Controls in Hybrid Trials

no code implementations • 15 May 2023 • Michael Valancius, Herb Pang, Jiawen Zhu, Stephen R Cole, Michele Jonsson Funk, Michael R Kosorok

We consider the challenges associated with causal inference in settings where data from a randomized trial is augmented with control data from an external source to improve efficiency in estimating the average treatment effect (ATE).

Causal Inference

Anomaly Detection under Distribution Shift

1 code implementation • ICCV 2023 • Tri Cao, Jiawen Zhu, Guansong Pang

Anomaly detection (AD) is a crucial machine learning task that aims to learn patterns from a set of normal training samples to identify abnormal samples in test data.

Anomaly Detection Test

Visual Prompt Multi-Modal Tracking

1 code implementation • CVPR 2023 • Jiawen Zhu, Simiao Lai, Xin Chen, Dong Wang, Huchuan Lu

To inherit the powerful representations of the foundation model, a natural modus operandi for multi-modal tracking is full fine-tuning on the RGB-based parameters.

Object Tracking

SRRT: Search Region Regulation Tracking

no code implementations • 10 Jul 2022 • Jiawen Zhu, Xin Chen, Pengyu Zhang, Xinying Wang, Dong Wang, Wenda Zhao, Huchuan Lu

Trackers tend to lose the target object due to the limited search region or be interfered with by distractors due to the excessive search region.

High-Performance Transformer Tracking

1 code implementation • 25 Mar 2022 • Xin Chen, Bin Yan, Jiawen Zhu, Huchuan Lu, Xiang Ruan, Dong Wang

First, we present a transformer tracking (named TransT) method based on the Siamese-like feature extraction backbone, the designed attention-based fusion mechanism, and the classification and regression head.

Vocal Bursts Intensity Prediction

AutoChart: A Dataset for Chart-to-Text Generation Task

no code implementations • RANLP 2021 • Jiawen Zhu, Jinye Ran, Roy Ka-Wei Lee, Kenny Choo, Zhi Li

The analytical description of charts is an exciting and important research area with many applications in academia and industry.

Text Generation

Transformer Tracking

1 code implementation • CVPR 2021 • Xin Chen, Bin Yan, Jiawen Zhu, Dong Wang, Xiaoyun Yang, Huchuan Lu

The correlation operation is a simple fusion manner to consider the similarity between the template and the search region.

Visual Object Tracking Visual Tracking