1 code implementation • journal 2023 • Chukwuemeka Clinton Atabansi, Jing Nie, Haijun Liu, Qianqian Song, Lingfeng Yan, Xichuan Zhou
Transformers have been widely used in many computer vision challenges and have shown the capability of producing better results than convolutional neural networks (CNNs).
no code implementations • 14 Jul 2023 • Haijun Liu, Xi Su, Xiangfei Shen, Lihui Chen, Xichuan Zhou
Our method introduces a separation training loss based on a latent binary mask to separately constrain the background and anomalies in the estimated image.
1 code implementation • 9 Dec 2020 • Haijun Liu, Yanxia Chai, Xiaoheng Tan, Dong Li, Xichuan Zhou
In this letter, we propose a conceptually simple and effective dual-granularity triplet loss for visible-thermal person re-identification (VT-ReID).
Ranked #2 on Cross-Modal Person Re-Identification on RegDB
1 code implementation • 14 Aug 2020 • Haijun Liu, Xiaoheng Tan, Xichuan Zhou
By well splitting the ResNet50 model to construct the modality-specific feature extracting network and modality-sharing feature embedding network, we experimentally demonstrate the effect of parameters sharing of two-stream network for VT Re-ID.
Ranked #1 on Cross-Modal Person Re-Identification on RegDB
Cross-Modality Person Re-identification Cross-Modal Person Re-Identification
1 code implementation • ICML 2020 • Xichuan Zhou, Yicong Peng, Chunqiao Long, Fengbo Ren, Cong Shi
Monocular multi-object detection and localization in 3D space has been proven to be a challenging task.
1 code implementation • 9 Jun 2020 • Xichuan Zhou, Kui Liu, Cong Shi, Haijun Liu, Ji Liu
Recent researches on information bottleneck shed new light on the continuous attempts to open the black box of neural signal encoding.
1 code implementation • CVPR 2020 • Fuxiang Huang, Lei Zhang, Yang Yang, Xichuan Zhou
Most of the existing image retrieval methods only focus on single-domain retrieval, which assumes that the distributions of retrieval databases and queries are similar.
no code implementations • 17 Jan 2020 • Xinzheng Zhang, Guo Liu, Ce Zhang, Peter M. Atkinson, Xiaoheng Tan, Xin Jian, Xichuan Zhou, Yongming Li
The prediction of this Phase is the set of changed and unchanged superpixels.
1 code implementation • 10 Sep 2019 • Zheyu Yan, Yiyu Shi, Wang Liao, Masanori Hashimoto, Xichuan Zhou, Cheng Zhuo
We are then able to analytically explore the weakness of a network and summarize the key findings for the impact of SIPP on different types of bits in a floating point parameter, layer-wise robustness within the same network and impact of network depth.
no code implementations • 21 Apr 2016 • Xichuan Zhou, Shengli Li, Kai Qin, Kunping Li, Fang Tang, Shengdong Hu, Shujun Liu, Zhi Lin
Deep neural networks are state-of-the-art models for understanding the content of images, video and raw input data.