1 code implementation • 19 Dec 2023 • Sihan Liu, Yiwei Ma, Xiaoqing Zhang, Haowei Wang, Jiayi Ji, Xiaoshuai Sun, Rongrong Ji
Referring Remote Sensing Image Segmentation (RRSIS) is a new challenge that combines computer vision and natural language processing, delineating specific regions in aerial images as described by textual queries.
1 code implementation • 30 Nov 2023 • Yiwei Ma, Yijun Fan, Jiayi Ji, Haowei Wang, Xiaoshuai Sun, Guannan Jiang, Annan Shu, Rongrong Ji
Nevertheless, a substantial domain gap exists between 2D images and 3D assets, primarily attributed to variations in camera-related attributes and the exclusive presence of foreground objects.
1 code implementation • 31 Oct 2023 • Shixiong Wang, Wei Dai, Haowei Wang, Geoffrey Ye Li
Therefore, we formulate robust waveform design problems by studying the worst-case channels and prove that the robustly-estimated performance is guaranteed to be attainable in real-world operation.
1 code implementation • 27 Oct 2023 • Danni Yang, Jiayi Ji, Xiaoshuai Sun, Haowei Wang, Yinan Li, Yiwei Ma, Rongrong Ji
Remarkably, our SS-PNG-NW+ outperforms fully-supervised models with only 30% and 50% supervision data, exceeding their performance by 0. 8% and 1. 1% respectively.
1 code implementation • 17 Oct 2023 • Haowei Wang, Jiayi Ji, Tianyu Guo, Yilong Yang, Yiyi Zhou, Xiaoshuai Sun, Rongrong Ji
To address this, we introduce two cascading modules based on the barycenter of the mask, which are Coordinate Guided Aggregation (CGA) and Barycenter Driven Localization (BDL), responsible for segmentation and detection, respectively.
1 code implementation • 14 Oct 2023 • Jiayi Ji, Haowei Wang, Changli Wu, Yiwei Ma, Xiaoshuai Sun, Rongrong Ji
The rising importance of 3D understanding, pivotal in computer vision, autonomous driving, and robotics, is evident.
1 code implementation • 31 Aug 2023 • Changli Wu, Yiwei Ma, Qi Chen, Haowei Wang, Gen Luo, Jiayi Ji, Xiaoshuai Sun
In 3D Referring Expression Segmentation (3D-RES), the earlier approach adopts a two-stage paradigm, extracting segmentation proposals and then matching them with referring expressions.
1 code implementation • 6 Aug 2023 • Haowei Wang, Jiji Tang, Jiayi Ji, Xiaoshuai Sun, Rongsheng Zhang, Yiwei Ma, Minda Zhao, Lincheng Li, Zeng Zhao, Tangjie Lv, Rongrong Ji
Insufficient synergy neglects the idea that a robust 3D representation should align with the joint vision-language space, rather than independently aligning with each modality.
1 code implementation • ICCV 2023 • Yiwei Ma, Xiaioqing Zhang, Xiaoshuai Sun, Jiayi Ji, Haowei Wang, Guannan Jiang, Weilin Zhuang, Rongrong Ji
Text-driven 3D stylization is a complex and crucial task in the fields of computer vision (CV) and computer graphics (CG), aimed at transforming a bare mesh to fit a target text.
no code implementations • 31 Jan 2023 • Shixiong Wang, Haowei Wang, Jean Honorio
Trustworthy machine learning aims at combating distributional uncertainties in training data distributions compared to population distributions.
1 code implementation • 9 Jan 2023 • Haowei Wang, Jiayi Ji, Yiyi Zhou, Yongjian Wu, Xiaoshuai Sun
Extensive experiments on the PNG benchmark dataset demonstrate the effectiveness and efficiency of our method.
no code implementations • 20 Dec 2022 • Shixiong Wang, Haowei Wang
Third, we show that generalization errors of machine learning models can be characterized using the distributional uncertainty of the nominal distribution and the robustness measures of these machine learning models, which is a new perspective to bound generalization errors, and therefore, explain the reason why distributionally robust machine learning models, Bayesian models, and regularization models tend to have smaller generalization errors in a unified manner.
no code implementations • 16 May 2022 • Haowei Wang, Ercong Zhang, Szu Hui Ng, Giulia Pedrielli
In this study, we propose a model aggregation method in the Bayesian optimization (MamBO) algorithm for efficiently solving high-dimensional large-scale optimization problems.
no code implementations • 10 May 2022 • Shouri Hu, Haowei Wang, Zhongxiang Dai, Bryan Kian Hsiang Low, Szu Hui Ng
To adapt the EI for better performance under cumulative regret, we introduce a novel quantity called the evaluation cost which is compared against the acquisition function, and with this, develop the expected improvement-cost (EIC) algorithm.