3 code implementations • 22 Apr 2024 • Xiaoning Liu, Zongwei Wu, Ao Li, Florin-Alexandru Vasluianu, Yulun Zhang, Shuhang Gu, Le Zhang, Ce Zhu, Radu Timofte, Zhi Jin, Hongjun Wu, Chenxi Wang, Haitao Ling, Yuanhao Cai, Hao Bian, Yuxin Zheng, Jing Lin, Alan Yuille, Ben Shao, Jin Guo, Tianli Liu, Mohao Wu, Yixu Feng, Shuo Hou, Haotian Lin, Yu Zhu, Peng Wu, Wei Dong, Jinqiu Sun, Yanning Zhang, Qingsen Yan, Wenbin Zou, Weipeng Yang, Yunxiang Li, Qiaomu Wei, Tian Ye, Sixiang Chen, Zhao Zhang, Suiyi Zhao, Bo wang, Yan Luo, Zhichao Zuo, Mingshen Wang, Junhu Wang, Yanyan Wei, Xiaopeng Sun, Yu Gao, Jiancheng Huang, Hongming Chen, Xiang Chen, Hui Tang, Yuanbin Chen, Yuanbo Zhou, Xinwei Dai, Xintao Qiu, Wei Deng, Qinquan Gao, Tong Tong, Mingjia Li, Jin Hu, Xinyu He, Xiaojie Guo, sabarinathan, K Uma, A Sasithradevi, B Sathya Bama, S. Mohamed Mansoor Roomi, V. Srivatsav, Jinjuan Wang, Long Sun, Qiuying Chen, Jiahong Shao, Yizhi Zhang, Marcos V. Conde, Daniel Feijoo, Juan C. Benito, Alvaro García, Jaeho Lee, Seongwan Kim, Sharif S M A, Nodirkhuja Khujaev, Roman Tsoy, Ali Murtaza, Uswah Khairuddin, Ahmad 'Athif Mohd Faudzi, Sampada Malagi, Amogh Joshi, Nikhil Akalwadi, Chaitra Desai, Ramesh Ashok Tabib, Uma Mudenagudi, Wenyi Lian, Wenjing Lian, Jagadeesh Kalyanshetti, Vijayalaxmi Ashok Aralikatti, Palani Yashaswini, Nitish Upasi, Dikshit Hegde, Ujwala Patil, Sujata C, Xingzhuo Yan, Wei Hao, Minghan Fu, Pooja Choksy, Anjali Sarvaiya, Kishor Upla, Kiran Raja, Hailong Yan, Yunkai Zhang, Baiang Li, Jingyi Zhang, Huan Zheng
This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results.
no code implementations • 4 Apr 2024 • Xu Wang, Tian Ye, Rajgopal Kannan, Viktor Prasanna
FACTUAL consists of two components: (1) Differing from existing works, a novel perturbation scheme that incorporates realistic physical adversarial attacks (such as OTSA) to build a supervised adversarial pre-training network.
no code implementations • 27 Mar 2024 • Jianshu Guo, Wenhao Chai, Jie Deng, Hsiang-Wei Huang, Tian Ye, Yichen Xu, Jiawei Zhang, Jenq-Neng Hwang, Gaoang Wang
Recent text-to-image (T2I) models have benefited from large-scale and high-quality data, demonstrating impressive performance.
no code implementations • 27 Mar 2024 • Tian Ye, Rajgopal Kannan, Viktor Prasanna, Carl Busart
Adversarial attacks have demonstrated the vulnerability of Machine Learning (ML) image classifiers in Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) systems.
no code implementations • 13 Mar 2024 • Zhonghan Zhao, Kewei Chen, Dongxu Guo, Wenhao Chai, Tian Ye, Yanting Zhang, Gaoang Wang
To assess organizational behavior, we design a series of navigation tasks in the Minecraft environment, which includes searching and exploring.
1 code implementation • 1 Feb 2024 • Jacob Fein-Ashley, Tian Ye, Sachini Wickramasinghe, Bingyi Zhang, Rajgopal Kannan, Viktor Prasanna
Our experimental results on benchmark grayscale image datasets demonstrate the effectiveness of the proposed model, achieving vastly lower latency (up to 16$\times$ less) and competitive or leading performance compared to other state-of-the-art image classification models on various domain-specific grayscale image classification datasets.
Ranked #16 on Image Classification on Fashion-MNIST
1 code implementation • 24 Jan 2024 • Zhaohu Xing, Tian Ye, Yijun Yang, Guang Liu, Lei Zhu
Our SegMamba, in contrast to Transformer-based methods, excels in whole volume feature modeling from a state space model standpoint, maintaining superior processing speed, even with volume features at a resolution of {$64\times 64\times 64$}.
1 code implementation • 14 Dec 2023 • Wenbin Zou, Hongxia Gao, Tian Ye, Liang Chen, Weipeng Yang, Shasha Huang, Hongsheng Chen, Sixiang Chen
In this paper, we propose Clearer Night Image Restoration with Vector-Quantized Codebook (VQCNIR) to achieve remarkable and consistent restoration outcomes on real-world and synthetic benchmarks.
no code implementations • 12 Dec 2023 • Jacob Fein-Ashley, Tian Ye, Rajgopal Kannan, Viktor Prasanna, Carl Busart
Synthetic Aperture Radar SAR Automatic Target Recognition ATR is a key technique of remote-sensing image recognition which can be supported by deep neural networks The existing works of SAR ATR mostly focus on improving the accuracy of the target recognition while ignoring the systems performance in terms of speed and storage which is critical to real-world applications of SAR ATR For decision-makers aiming to identify a proper deep learning model to deploy in a SAR ATR system it is important to understand the performance of different candidate deep learning models and determine the best model accordingly This paper comprehensively benchmarks several advanced deep learning models for SAR ATR with multiple distinct SAR imagery datasets Specifically we train and test five SAR image classifiers based on Residual Neural Networks ResNet18 ResNet34 ResNet50 Graph Neural Network GNN and Vision Transformer for Small-Sized Datasets (SS-ViT) We select three datasets MSTAR GBSAR and SynthWakeSAR that offer heterogeneity We evaluate and compare the five classifiers concerning their classification accuracy runtime performance in terms of inference throughput and analytical performance in terms of number of parameters number of layers model size and number of operations Experimental results show that the GNN classifier outperforms with respect to throughput and latency However it is also shown that no clear model winner emerges from all of our chosen metrics and a one model rules all case is doubtful in the domain of SAR ATR
no code implementations • 5 Dec 2023 • Tian Ye, Rajgopal Kannan, Viktor Prasanna, Carl Busart, Lance Kaplan
Instead, adversarial attacks should be able to be implemented by physical actions, for example, placing additional false objects as scatterers around the on-ground target to perturb the SAR image and fool the SAR ATR.
no code implementations • 26 Nov 2023 • Zhonghan Zhao, Wenhao Chai, Xuan Wang, Li Boyi, Shengyu Hao, Shidong Cao, Tian Ye, Jenq-Neng Hwang, Gaoang Wang
Vision perception involves the interpretation of visual information in the environment, which is then integrated into the LLMs component with agent state and task instruction.
1 code implementation • 20 Nov 2023 • Chunming He, Chengyu Fang, Yulun Zhang, Tian Ye, Kai Li, Longxiang Tang, Zhenhua Guo, Xiu Li, Sina Farsiu
These priors are subsequently utilized by RGformer to guide the decomposition of image features into their respective reflectance and illumination components.
1 code implementation • 24 Oct 2023 • Jinbin Bai, Zhen Dong, Aosong Feng, Xiao Zhang, Tian Ye, Kaicheng Zhou, Mike Zheng Shou
In the field of image processing, applying intricate semantic modifications within existing images remains an enduring challenge.
1 code implementation • ICCV 2023 • Sixiang Chen, Tian Ye, Jinbin Bai, ErKang Chen, Jun Shi, Lei Zhu
In the real world, image degradations caused by rain often exhibit a combination of rain streaks and raindrops, thereby increasing the challenges of recovering the underlying clean image.
1 code implementation • 31 Jul 2023 • Enxin Song, Wenhao Chai, Guanhong Wang, Yucheng Zhang, Haoyang Zhou, Feiyang Wu, Haozhe Chi, Xun Guo, Tian Ye, Yanting Zhang, Yan Lu, Jenq-Neng Hwang, Gaoang Wang
Recently, integrating video foundation models and large language models to build a video understanding system can overcome the limitations of specific pre-defined vision tasks.
Video-based Generative Performance Benchmarking (Consistency) Video-based Generative Performance Benchmarking (Contextual Understanding) +10
no code implementations • 29 Jun 2023 • Zhenyu Zhang, Wenhao Chai, Zhongyu Jiang, Tian Ye, Mingli Song, Jenq-Neng Hwang, Gaoang Wang
In this paper, we propose MPM, a unified 2D-3D human pose representation framework via masked pose modeling.
1 code implementation • 16 May 2023 • Yun Liu, Zhongsheng Yan, Sixiang Chen, Tian Ye, Wenqi Ren, ErKang Chen
Extensive experiments on several synthetic and real-world datasets demonstrate the superiority of our NightHazeFormer over state-of-the-art nighttime haze removal methods in terms of both visually and quantitatively.
1 code implementation • 15 May 2023 • Jingxia Jiang, Tian Ye, Jinbin Bai, Sixiang Chen, Wenhao Chai, Shi Jun, Yun Liu, ErKang Chen
In this work, we propose the Five A$^{+}$ Network (FA$^{+}$Net), a highly efficient and lightweight real-time underwater image enhancement network with only $\sim$ 9k parameters and $\sim$ 0. 01s processing time.
no code implementations • 13 Mar 2023 • Sixiang Chen, Tian Ye, Jun Shi, Yun Liu, Jingxia Jiang, ErKang Chen, Peng Chen
Varicolored haze caused by chromatic casts poses haze removal and depth estimation challenges.
no code implementations • 23 Feb 2023 • Jingxia Jiang, Jinbin Bai, Yun Liu, Junjie Yin, Sixiang Chen, Tian Ye, ErKang Chen
Underwater images typically experience mixed degradations of brightness and structure caused by the absorption and scattering of light by suspended particles.
no code implementations • ICCV 2023 • Tian Ye, Sixiang Chen, Jinbin Bai, Jun Shi, Chenghao Xue, Jingxia Jiang, Junjie Yin, ErKang Chen, Yun Liu
Inspired by recent advancements in codebook and vector quantization (VQ) techniques, we present a novel Adverse Weather Removal network with Codebook Priors (AWRCP) to address the problem of unified adverse weather removal.
no code implementations • 3 Oct 2022 • Sixiang Chen, Tian Ye, Yun Liu, ErKang Chen
Recently, image restoration transformers have achieved comparable performance with previous state-of-the-art CNNs.
3 code implementations • 23 Aug 2022 • Ren Yang, Radu Timofte, Qi Zhang, Lin Zhang, Fanglong Liu, Dongliang He, Fu Li, He Zheng, Weihang Yuan, Pavel Ostyakov, Dmitry Vyal, Magauiya Zhussip, Xueyi Zou, Youliang Yan, Lei LI, Jingzhu Tang, Ming Chen, Shijie Zhao, Yu Zhu, Xiaoran Qin, Chenghua Li, Cong Leng, Jian Cheng, Claudio Rota, Marco Buzzelli, Simone Bianco, Raimondo Schettini, Dafeng Zhang, Feiyu Huang, Shizhuo Liu, Xiaobing Wang, Zhezhu Jin, Bingchen Li, Xin Li, Mingxi Li, Ding Liu, Wenbin Zou, Peijie Dong, Tian Ye, Yunchen Zhang, Ming Tan, Xin Niu, Mustafa Ayazoglu, Marcos Conde, Ui-Jin Choi, Zhuang Jia, Tianyu Xu, Yijian Zhang, Mao Ye, Dengyan Luo, Xiaofeng Pan, Liuhan Peng
The homepage of this challenge is at https://github. com/RenYang-home/AIM22_CompressSR.
1 code implementation • 20 Aug 2022 • Sixiang Chen, Tian Ye, Yun Liu, ErKang Chen
Due to various and complicated snow degradations, single image desnowing is a challenging image restoration task.
no code implementations • 12 Jul 2022 • Tian Ye, Sixiang Chen, Yun Liu, Yi Ye, ErKang Chen
In winter scenes, the degradation of images taken under snow can be pretty complex, where the spatial distribution of snowy degradation is varied from image to image.
no code implementations • 12 Jul 2022 • Sixiang Chen, Tian Ye, Yun Liu, Taodong Liao, Jingxia Jiang, ErKang Chen, Peng Chen
Snow removal causes challenges due to its characteristic of complex degradations.
1 code implementation • 27 Jun 2022 • Peijie Dong, Xin Niu, Lujun Li, Linzhen Xie, Wenbin Zou, Tian Ye, Zimian Wei, Hengyue Pan
In this paper, we present Prior-Guided One-shot NAS (PGONAS) to strengthen the ranking correlation of supernets.
2 code implementations • 11 May 2022 • Yawei Li, Kai Zhang, Radu Timofte, Luc van Gool, Fangyuan Kong, Mingxi Li, Songwei Liu, Zongcai Du, Ding Liu, Chenhui Zhou, Jingyi Chen, Qingrui Han, Zheyuan Li, Yingqi Liu, Xiangyu Chen, Haoming Cai, Yu Qiao, Chao Dong, Long Sun, Jinshan Pan, Yi Zhu, Zhikai Zong, Xiaoxiao Liu, Zheng Hui, Tao Yang, Peiran Ren, Xuansong Xie, Xian-Sheng Hua, Yanbo Wang, Xiaozhong Ji, Chuming Lin, Donghao Luo, Ying Tai, Chengjie Wang, Zhizhong Zhang, Yuan Xie, Shen Cheng, Ziwei Luo, Lei Yu, Zhihong Wen, Qi Wu1, Youwei Li, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Yuanfei Huang, Meiguang Jin, Hua Huang, Jing Liu, Xinjian Zhang, Yan Wang, Lingshun Long, Gen Li, Yuanfan Zhang, Zuowei Cao, Lei Sun, Panaetov Alexander, Yucong Wang, Minjie Cai, Li Wang, Lu Tian, Zheyuan Wang, Hongbing Ma, Jie Liu, Chao Chen, Yidong Cai, Jie Tang, Gangshan Wu, Weiran Wang, Shirui Huang, Honglei Lu, Huan Liu, Keyan Wang, Jun Chen, Shi Chen, Yuchun Miao, Zimo Huang, Lefei Zhang, Mustafa Ayazoğlu, Wei Xiong, Chengyi Xiong, Fei Wang, Hao Li, Ruimian Wen, Zhijing Yang, Wenbin Zou, Weixin Zheng, Tian Ye, Yuncheng Zhang, Xiangzhen Kong, Aditya Arora, Syed Waqas Zamir, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Dandan Gaoand Dengwen Zhouand Qian Ning, Jingzhu Tang, Han Huang, YuFei Wang, Zhangheng Peng, Haobo Li, Wenxue Guan, Shenghua Gong, Xin Li, Jun Liu, Wanjun Wang, Dengwen Zhou, Kun Zeng, Hanjiang Lin, Xinyu Chen, Jinsheng Fang
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.
no code implementations • 19 Apr 2022 • Tian Ye, Sixiang Chen, Yun Liu, ErKang Chen, Yuche Li
A single expert network efficiently addresses specific degradation in nasty winter scenes relying on the compact architecture and three novel components.
1 code implementation • 19 Apr 2022 • Wenbin Zou, Tian Ye, Weixin Zheng, Yunchen Zhang, Liang Chen, Yi Wu
Recently, deep learning has been successfully applied to the single-image super-resolution (SISR) with remarkable performance.
1 code implementation • 21 Mar 2022 • Tian Ye, Sixiang Chen, Yun Liu, Yi Ye, ErKang Chen, Yuche Li
To this end, we propose a neural rendering method for underwater imaging, dubbed UWNR (Underwater Neural Rendering).
no code implementations • 17 Mar 2022 • Tian Ye, Yun Liu, Yunchen Zhang, Sixiang Chen, ErKang Chen
Specifically, we first devise two siamese networks: a teacher network in the synthetic domain and a student network in the real domain, and then optimize them in a mutual learning manner by leveraging EMA and joint loss.
1 code implementation • 18 Nov 2021 • Tian Ye, Mingchao Jiang, Yunchen Zhang, Liang Chen, ErKang Chen, Pen Chen, Zhiyong Lu
However, due to the paradox caused by the variation of real captured haze and the fixed degradation parameters of the current networks, the generalization ability of recent dehazing methods on real-world hazy images is not ideal. To address the problem of modeling real-world haze degradation, we propose to solve this problem by perceiving and modeling density for uneven haze distribution.
Ranked #5 on Image Dehazing on Haze4k
1 code implementation • 12 Sep 2021 • Tian Ye, ErKang Chen, XinRui Huang, Peng Chen
This paper proposes an end-to-end Efficient Re-parameterizationResidual Attention Network(ERRA-Net) to directly restore the nonhomogeneous hazy image.
no code implementations • NeurIPS 2021 • Tian Ye, Simon S. Du
We study the asymmetric low-rank factorization problem: \[\min_{\mathbf{U} \in \mathbb{R}^{m \times d}, \mathbf{V} \in \mathbb{R}^{n \times d}} \frac{1}{2}\|\mathbf{U}\mathbf{V}^\top -\mathbf{\Sigma}\|_F^2\] where $\mathbf{\Sigma}$ is a given matrix of size $m \times n$ and rank $d$.
no code implementations • ECCV 2020 • Senthil Purushwalkam, Tian Ye, Saurabh Gupta, Abhinav Gupta
During training, given a pair of videos, we compute cycles that connect patches in a given frame in the first video by matching through frames in the second video.
no code implementations • 19 Jun 2020 • Tian Ye, Peijun Xiao, Ruoyu Sun
In the infrequent communication setting, DEED combined with Federated averaging requires a smaller total number of bits than Federated Averaging.
1 code implementation • 21 Jun 2019 • Lu Haonan, Seth H. Huang, Tian Ye, Guo Xiuyan
In this work, we present graph star net (GraphStar), a novel and unified graph neural net architecture which utilizes message-passing relay and attention mechanism for multiple prediction tasks - node classification, graph classification and link prediction.
Ranked #1 on Link Prediction on Pubmed (biased evaluation)
no code implementations • ECCV 2018 • Tian Ye, Xiaolong Wang, James Davidson, Abhinav Gupta
In order to demonstrate that our system models these underlying physical properties, we train our model on collisions of different shapes (cube, cone, cylinder, spheres etc.)