Search Results for author: Long Lan

Found 18 papers, 6 papers with code

Analysis of Video Quality Datasets via Design of Minimalistic Video Quality Models

no code implementations26 Jul 2023 Wei Sun, Wen Wen, Xiongkuo Min, Long Lan, Guangtao Zhai, Kede Ma

By minimalistic, we restrict our family of BVQA models to build only upon basic blocks: a video preprocessor (for aggressive spatiotemporal downsampling), a spatial quality analyzer, an optional temporal quality analyzer, and a quality regressor, all with the simplest possible instantiations.

Blind Image Quality Assessment Video Quality Assessment +1

RefSAM: Efficiently Adapting Segmenting Anything Model for Referring Video Object Segmentation

1 code implementation3 Jul 2023 Yonglin Li, Jing Zhang, Xiao Teng, Long Lan

The Segment Anything Model (SAM) has gained significant attention for its impressive performance in image segmentation.

Image Segmentation Referring Expression +3

CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification

no code implementations8 Jun 2023 Nan Yin, Li Shen, Mengzhu Wang, Long Lan, Zeyu Ma, Chong Chen, Xian-Sheng Hua, Xiao Luo

Although graph neural networks (GNNs) have achieved impressive achievements in graph classification, they often need abundant task-specific labels, which could be extensively costly to acquire.

Contrastive Learning Domain Adaptation +2

MotionTrack: Learning Motion Predictor for Multiple Object Tracking

no code implementations5 Jun 2023 Changcheng Xiao, Qiong Cao, Yujie Zhong, Long Lan, Xiang Zhang, Huayue Cai, Zhigang Luo, DaCheng Tao

Despite these developments, the task of accurately tracking objects in scenarios with homogeneous appearance and heterogeneous motion remains challenging due to the insufficient discriminability of ReID features and the predominant use of linear motion models in MOT.

Ranked #8 on Multi-Object Tracking on DanceTrack (using extra training data)

motion prediction Multi-Object Tracking +1

Null-text Guidance in Diffusion Models is Secretly a Cartoon-style Creator

no code implementations11 May 2023 Jing Zhao, Heliang Zheng, Chaoyue Wang, Long Lan, Wanrong Huang, Wenjing Yang

Specifically, we proposed two disturbance methods, i. e., Rollback disturbance (Back-D) and Image disturbance (Image-D), to construct misalignment between the noisy images used for predicting null-text guidance and text guidance (subsequently referred to as \textbf{null-text noisy image} and \textbf{text noisy image} respectively) in the sampling process.

MagicFusion: Boosting Text-to-Image Generation Performance by Fusing Diffusion Models

no code implementations23 Mar 2023 Jing Zhao, Heliang Zheng, Chaoyue Wang, Long Lan, Wenjing Yang

The advent of open-source AI communities has produced a cornucopia of powerful text-guided diffusion models that are trained on various datasets.

Towards Radar Emitter Recognition in Changing Environments with Domain Generalization

no code implementations18 Feb 2023 Honglin Wu, Xueqiong Li, Long Lan, Liyang Xu, Yuhua Tang

Analyzing radar signals from complex Electronic Warfare (EW) environment is a non-trivial task. However, in the real world, the changing EW environment results in inconsistent signal distribution, such as the pulse repetition interval (PRI) mismatch between different detected scenes. In this paper, we propose a novel domain generalization framework to improve the adaptability of signal recognition in changing environments. Specifically, we first design several noise generators to simulate varied scenes.

Domain Generalization

1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results

no code implementations24 Nov 2022 Benjamin Kiefer, Matej Kristan, Janez Perš, Lojze Žust, Fabio Poiesi, Fabio Augusto de Alcantara Andrade, Alexandre Bernardino, Matthew Dawkins, Jenni Raitoharju, Yitong Quan, Adem Atmaca, Timon Höfer, Qiming Zhang, Yufei Xu, Jing Zhang, DaCheng Tao, Lars Sommer, Raphael Spraul, Hangyue Zhao, Hongpu Zhang, Yanyun Zhao, Jan Lukas Augustin, Eui-ik Jeon, Impyeong Lee, Luca Zedda, Andrea Loddo, Cecilia Di Ruberto, Sagar Verma, Siddharth Gupta, Shishir Muralidhara, Niharika Hegde, Daitao Xing, Nikolaos Evangeliou, Anthony Tzes, Vojtěch Bartl, Jakub Špaňhel, Adam Herout, Neelanjan Bhowmik, Toby P. Breckon, Shivanand Kundargi, Tejas Anvekar, Chaitra Desai, Ramesh Ashok Tabib, Uma Mudengudi, Arpita Vats, Yang song, Delong Liu, Yonglin Li, Shuman Li, Chenhao Tan, Long Lan, Vladimir Somers, Christophe De Vleeschouwer, Alexandre Alahi, Hsiang-Wei Huang, Cheng-Yen Yang, Jenq-Neng Hwang, Pyong-Kun Kim, Kwangju Kim, Kyoungoh Lee, Shuai Jiang, Haiwen Li, Zheng Ziqiang, Tuan-Anh Vu, Hai Nguyen-Truong, Sai-Kit Yeung, Zhuang Jia, Sophia Yang, Chih-Chung Hsu, Xiu-Yu Hou, Yu-An Jhang, Simon Yang, Mau-Tsuen Yang

The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection.

object-detection Object Detection +1

Perceptual Quality Assessment of Virtual Reality Videos in the Wild

1 code implementation13 Jun 2022 Wen Wen, Mu Li, Yiru Yao, Xiangjie Sui, Yabin Zhang, Long Lan, Yuming Fang, Kede Ma

Investigating how people perceive virtual reality videos in the wild (\ie, those captured by everyday users) is a crucial and challenging task in VR-related applications due to complex \textit{authentic} distortions localized in space and time.

Saliency Detection Video Quality Assessment

APT-36K: A Large-scale Benchmark for Animal Pose Estimation and Tracking

4 code implementations12 Jun 2022 Yuxiang Yang, Junjie Yang, Yufei Xu, Jing Zhang, Long Lan, DaCheng Tao

Based on APT-36K, we benchmark several representative models on the following three tracks: (1) supervised animal pose estimation on a single frame under intra- and inter-domain transfer learning settings, (2) inter-species domain generalization test for unseen animals, and (3) animal pose estimation with animal tracking.

Animal Pose Estimation Domain Generalization +1

Bilateral Dependency Optimization: Defending Against Model-inversion Attacks

1 code implementation11 Jun 2022 Xiong Peng, Feng Liu, Jingfen Zhang, Long Lan, Junjie Ye, Tongliang Liu, Bo Han

To defend against MI attacks, previous work utilizes a unilateral dependency optimization strategy, i. e., minimizing the dependency between inputs (i. e., features) and outputs (i. e., labels) during training the classifier.

On the Equity of Nuclear Norm Maximization in Unsupervised Domain Adaptation

no code implementations12 Apr 2022 Wenju Zhang, Xiang Zhang, Qing Liao, Long Lan, Mengzhu Wang, Wei Wang, Baoyun Peng, Zhengming Ding

Nuclear norm maximization has shown the power to enhance the transferability of unsupervised domain adaptation model (UDA) in an empirical scheme.

Image Classification Unsupervised Domain Adaptation

Meta Attention For Off-Policy Actor-Critic

no code implementations29 Sep 2021 Jiateng Huang, Wanrong Huang, Long Lan, Dan Wu

In this paper, we propose a meta attention method for state-based reinforcement learning tasks, which combines attention mechanism and meta-learning based on the Off-Policy Actor-Critic framework.

Continuous Control Decision Making +3

TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation

1 code implementation NeurIPS 2021 Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, William K. Cheung, James T. Kwok

To this end, we propose a target orientated hypothesis adaptation network (TOHAN) to solve the FHA problem, where we generate highly-compatible unlabeled data (i. e., an intermediate domain) to help train a target-domain classifier.

Domain Adaptation

Meta Discovery: Learning to Discover Novel Classes given Very Limited Data

1 code implementation ICLR 2022 Haoang Chi, Feng Liu, Bo Han, Wenjing Yang, Long Lan, Tongliang Liu, Gang Niu, Mingyuan Zhou, Masashi Sugiyama

In this paper, we demystify assumptions behind NCD and find that high-level semantic features should be shared among the seen and unseen classes.

Clustering Meta-Learning +1

Improving Unsupervised Domain Adaptation by Reducing Bi-level Feature Redundancy

no code implementations28 Dec 2020 Mengzhu Wang, Xiang Zhang, Long Lan, Wei Wang, Huibin Tan, Zhigang Luo

In this paper, we emphasize the significance of reducing feature redundancy for improving UDA in a bi-level way.

Unsupervised Domain Adaptation

Enhancing the Association in Multi-Object Tracking via Neighbor Graph

no code implementations1 Jul 2020 Tianyi Liang, Long Lan, Zhigang Luo

Most modern multi-object tracking (MOT) systems follow the tracking-by-detection paradigm.

Multi-Object Tracking

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