2 code implementations • 16 Apr 2024 • Bin Ren, Nancy Mehta, Radu Timofte, Hongyuan Yu, Cheng Wan, Yuxin Hong, Bingnan Han, Zhuoyuan Wu, Yajun Zou, Yuqing Liu, Jizhe Li, Keji He, Chao Fan, Heng Zhang, Xiaolin Zhang, Xuanwu Yin, Kunlong Zuo, Bohao Liao, Peizhe Xia, Long Peng, Zhibo Du, Xin Di, Wangkai Li, Yang Wang, Wei Zhai, Renjing Pei, Jiaming Guo, Songcen Xu, Yang Cao, ZhengJun Zha, Yan Wang, Yi Liu, Qing Wang, Gang Zhang, Liou Zhang, Shijie Zhao, Long Sun, Jinshan Pan, Jiangxin Dong, Jinhui Tang, Xin Liu, Min Yan, Menghan Zhou, Yiqiang Yan, Yixuan Liu, Wensong Chan, Dehua Tang, Dong Zhou, Li Wang, Lu Tian, Barsoum Emad, Bohan Jia, Junbo Qiao, Yunshuai Zhou, Yun Zhang, Wei Li, Shaohui Lin, Shenglong Zhou, Binbin Chen, Jincheng Liao, Suiyi Zhao, Zhao Zhang, Bo wang, Yan Luo, Yanyan Wei, Feng Li, Mingshen Wang, Yawei Li, Jinhan Guan, Dehua Hu, Jiawei Yu, Qisheng Xu, Tao Sun, Long Lan, Kele Xu, Xin Lin, Jingtong Yue, Lehan Yang, Shiyi Du, Lu Qi, Chao Ren, Zeyu Han, YuHan Wang, Chaolin Chen, Haobo Li, Mingjun Zheng, Zhongbao Yang, Lianhong Song, Xingzhuo Yan, Minghan Fu, Jingyi Zhang, Baiang Li, Qi Zhu, Xiaogang Xu, Dan Guo, Chunle Guo, Jiadi Chen, Huanhuan Long, Chunjiang Duanmu, Xiaoyan Lei, Jie Liu, Weilin Jia, Weifeng Cao, Wenlong Zhang, Yanyu Mao, Ruilong Guo, Nihao Zhang, Qian Wang, Manoj Pandey, Maksym Chernozhukov, Giang Le, Shuli Cheng, Hongyuan Wang, Ziyan Wei, Qingting Tang, Liejun Wang, Yongming Li, Yanhui Guo, Hao Xu, Akram Khatami-Rizi, Ahmad Mahmoudi-Aznaveh, Chih-Chung Hsu, Chia-Ming Lee, Yi-Shiuan Chou, Amogh Joshi, Nikhil Akalwadi, Sampada Malagi, Palani Yashaswini, Chaitra Desai, Ramesh Ashok Tabib, Ujwala Patil, Uma Mudenagudi
In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking.
1 code implementation • 20 Mar 2024 • Jingyi Wang, Xiaobo Xia, Long Lan, Xinghao Wu, Jun Yu, Wenjing Yang, Bo Han, Tongliang Liu
Given data with noisy labels, over-parameterized deep networks suffer overfitting mislabeled data, resulting in poor generalization.
no code implementations • 13 Mar 2024 • Long Lan, Fengxiang Wang, Shuyan Li, Xiangtao Zheng, Zengmao Wang, Xinwang Liu
Directly fine-tuning VLMs for RS-FGSC often encounters the challenge of overfitting the seen classes, resulting in suboptimal generalization to unseen classes, which highlights the difficulty in differentiating complex backgrounds and capturing distinct ship features.
no code implementations • 27 Nov 2023 • Xiang Li, Long Lan, Husam Lahza, Shaowu Yang, Shuihua Wang, Wenjing Yang, Hengzhu Liu, Yudong Zhang
EAFP-Med can efficiently extract lesion features from various medical images based on prompts, enhancing the model's performance.
no code implementations • 26 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.
1 code implementation • 3 Jul 2023 • Yonglin Li, Jing Zhang, Xiao Teng, Long Lan
However, it lacks proficiency in referring video object segmentation (RVOS) due to the need for precise user-interactive prompts and a limited understanding of different modalities, such as language and vision.
no code implementations • 8 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.
no code implementations • 5 Jun 2023 • Changcheng Xiao, Qiong Cao, Yujie Zhong, Long Lan, Xiang Zhang, Zhigang Luo, DaCheng Tao
This challenge arises from two main factors: the insufficient discriminability of ReID features and the predominant utilization of linear motion models in MOT.
Ranked #4 on Multi-Object Tracking on SportsMOT
no code implementations • 11 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.
no code implementations • ICCV 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.
no code implementations • 18 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.
no code implementations • ICCV 2023 • Haotian Wang, Haoang Chi, Wenjing Yang, Zhipeng Lin, Mingyang Geng, Long Lan, Jing Zhang, DaCheng Tao
As a complementary of SDPA, we also propose Target-Combined Deployment Authorization (TPDA), where unauthorized domains are partially accessible, and simplify the DSO method to a perturbation operation on the pseudo predictions, referred to as Target-Dependent Domain-Specified Optimization (TDSO).
no code implementations • 24 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.
1 code implementation • 13 Jun 2022 • Wen Wen, Mu Li, Yiru Yao, Xiangjie Sui, Yabin Zhang, Long Lan, Yuming Fang, Kede Ma
Investigating how people perceive virtual reality (VR) videos in the wild (i. e., those captured by everyday users) is a crucial and challenging task in VR-related applications due to complex authentic distortions localized in space and time.
4 code implementations • 12 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.
2 code implementations • 11 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.
no code implementations • 21 Apr 2022 • Long Lan, Xiao Teng, Jing Zhang, Xiang Zhang, DaCheng Tao
To purify the label noise, we propose to take advantage of the knowledge of teacher model in an offline scheme.
Knowledge Distillation Unsupervised Person Re-Identification
no code implementations • 12 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.
no code implementations • 29 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.
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.
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.
no code implementations • 28 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.
no code implementations • 1 Jul 2020 • Tianyi Liang, Long Lan, Zhigang Luo
Most modern multi-object tracking (MOT) systems follow the tracking-by-detection paradigm.