no code implementations • ECCV 2020 • Xiangyu He, Zitao Mo, Ke Cheng, Weixiang Xu, Qinghao Hu, Peisong Wang, Qingshan Liu, Jian Cheng
The matrix composed of basis vectors is referred to as the proxy matrix, and auxiliary variables serve as the coefficients of this linear combination.
1 code implementation • ICML 2020 • Peisong Wang, Qiang Chen, Xiangyu He, Jian Cheng
Network quantization is essential for deploying deep models to IoT devices due to the high efficiency, no matter on special hardware like TPU or general hardware like CPU and GPU.
no code implementations • 17 Feb 2024 • Yuhan Li, Peisong Wang, ZHIXUN LI, Jeffrey Xu Yu, Jia Li
The results underscore the effectiveness of our model in achieving significant cross-dataset zero-shot transferability, opening pathways for the development of graph foundation models.
1 code implementation • 21 Nov 2023 • Yuhan Li, ZHIXUN LI, Peisong Wang, Jia Li, Xiangguo Sun, Hong Cheng, Jeffrey Xu Yu
First of all, we propose a new taxonomy, which organizes existing methods into three categories based on the role (i. e., enhancer, predictor, and alignment component) played by LLMs in graph-related tasks.
1 code implementation • 20 Oct 2022 • Marcos V. Conde, Radu Timofte, Yibin Huang, Jingyang Peng, Chang Chen, Cheng Li, Eduardo Pérez-Pellitero, Fenglong Song, Furui Bai, Shuai Liu, Chaoyu Feng, Xiaotao Wang, Lei Lei, Yu Zhu, Chenghua Li, Yingying Jiang, Yong A, Peisong Wang, Cong Leng, Jian Cheng, Xiaoyu Liu, Zhicun Yin, Zhilu Zhang, Junyi Li, Ming Liu, WangMeng Zuo, Jun Jiang, Jinha Kim, Yue Zhang, Beiji Zou, Zhikai Zong, Xiaoxiao Liu, Juan Marín Vega, Michael Sloth, Peter Schneider-Kamp, Richard Röttger, Furkan Kınlı, Barış Özcan, Furkan Kıraç, Li Leyi, SM Nadim Uddin, Dipon Kumar Ghosh, Yong Ju Jung
Cameras capture sensor RAW images and transform them into pleasant RGB images, suitable for the human eyes, using their integrated Image Signal Processor (ISP).
1 code implementation • 4 Apr 2022 • Weixiang Xu, Xiangyu He, Tianli Zhao, Qinghao Hu, Peisong Wang, Jian Cheng
The latest STTN shows that ResNet-18 with ternary weights and ternary activations achieves up to 68. 2% Top-1 accuracy on ImageNet.
no code implementations • CVPR 2022 • Anda Cheng, Peisong Wang, Xi Sheryl Zhang, Jian Cheng
User-level differential privacy (DP) provides certifiable privacy guarantees to the information that is specific to any user's data in federated learning.
no code implementations • 19 Jan 2022 • Zhexin Li, Tong Yang, Peisong Wang, Jian Cheng
In this paper, we propose a fully differentiable quantization method for vision transformer (ViT) named as Q-ViT, in which both of the quantization scales and bit-widths are learnable parameters.
1 code implementation • 16 Oct 2021 • Anda Cheng, Jiaxing Wang, Xi Sheryl Zhang, Qiang Chen, Peisong Wang, Jian Cheng
In light of this missing, we propose the very first framework that employs neural architecture search to automatic model design for private deep learning, dubbed as DPNAS.
no code implementations • ICCV 2021 • Weihan Chen, Peisong Wang, Jian Cheng
Finally, based on the above simplification, we show that the original problem can be reformulated as a Multiple-Choice Knapsack Problem (MCKP) and propose a greedy search algorithm to solve it efficiently.
no code implementations • 12 Oct 2021 • Weixiang Xu, Qiang Chen, Xiangyu He, Peisong Wang, Jian Cheng
Binary Neural Networks (BNNs) rely on a real-valued auxiliary variable W to help binary training.
no code implementations • 21 Jan 2021 • Xiangyu He, Qinghao Hu, Peisong Wang, Jian Cheng
Convolutional neural networks are able to learn realistic image priors from numerous training samples in low-level image generation and restoration.
1 code implementation • 13 Nov 2019 • Xiangyu He, Zitao Mo, Qiang Chen, Anda Cheng, Peisong Wang, Jian Cheng
Many successful learning targets such as minimizing dice loss and cross-entropy loss have enabled unprecedented breakthroughs in segmentation tasks.
Ranked #35 on Semantic Segmentation on PASCAL Context
1 code implementation • 19 Oct 2019 • Qiang Chen, Anda Cheng, Xiangyu He, Peisong Wang, Jian Cheng
Object location is fundamental to panoptic segmentation as it is related to all things and stuff in the image scene.
Ranked #17 on Panoptic Segmentation on COCO test-dev
no code implementations • 24 Sep 2019 • Fanrong Li, Zitao Mo, Peisong Wang, Zejian Liu, Jiayun Zhang, Gang Li, Qinghao Hu, Xiangyu He, Cong Leng, Yang Zhang, Jian Cheng
As a case study, we evaluate our object detection system on a real-world surveillance video with input size of 512x512, and it turns out that the system can achieve an inference speed of 18 fps at the cost of 6. 9W (with display) with an mAP of 66. 4 verified on the PASCAL VOC 2012 dataset.
1 code implementation • 23 Jul 2019 • Xiangyu He, Ke Cheng, Qiang Chen, Qinghao Hu, Peisong Wang, Jian Cheng
Long-range dependencies modeling, widely used in capturing spatiotemporal correlation, has shown to be effective in CNN dominated computer vision tasks.
Ranked #208 on Object Detection on COCO test-dev
no code implementations • ECCV 2018 • Qinghao Hu, Gang Li, Peisong Wang, Yifan Zhang, Jian Cheng
In this paper, we propose a novel semi-binary decomposition method which decomposes a matrix into two binary matrices and a diagonal matrix.
1 code implementation • CVPR 2018 • Peisong Wang, Qinghao Hu, Yifan Zhang, Chunjie Zhang, Yang Liu, Jian Cheng
In this paper, we propose a simple yet effective Two-Step Quantization (TSQ) framework, by decomposing the network quantization problem into two steps: code learning and transformation function learning based on the learned codes.
no code implementations • 8 Feb 2018 • Qinghao Hu, Peisong Wang, Jian Cheng
To achieve this goal, we propose a novel approach named BWNH to train Binary Weight Networks via Hashing.
no code implementations • 3 Feb 2018 • Jian Cheng, Peisong Wang, Gang Li, Qinghao Hu, Hanqing Lu
As for hardware implementation of deep neural networks, a batch of accelerators based on FPGA/ASIC have been proposed in recent years.
no code implementations • CVPR 2017 • Peisong Wang, Jian Cheng
In recent years, Deep Neural Networks (DNN) based methods have achieved remarkable performance in a wide range of tasks and have been among the most powerful and widely used techniques in computer vision.