no code implementations • 15 Apr 2024 • Mude Hui, Siwei Yang, Bingchen Zhao, Yichun Shi, Heng Wang, Peng Wang, Yuyin Zhou, Cihang Xie
This study introduces HQ-Edit, a high-quality instruction-based image editing dataset with around 200, 000 edits.
1 code implementation • 23 Mar 2024 • Siwei Yang, Xianhang Li, Jieru Mei, Jieneng Chen, Cihang Xie, Yuyin Zhou
We identify that the Decoder-only 3D-TransUNet model should offer enhanced efficacy in the segmentation of brain metastases, as indicated by our 5-fold cross-validation on the training set.
1 code implementation • 14 Feb 2024 • Siwei Yang, Bingchen Zhao, Cihang Xie
This paper introduces AQA-Bench, a novel benchmark to assess the sequential reasoning capabilities of large language models (LLMs) in algorithmic contexts, such as depth-first search (DFS).
no code implementations • 14 Sep 2023 • Shiqiao Meng, Zonglin Di, Siwei Yang, Yin Wang
Our extensive experimental results show that the prediction accuracy increases with the amount of the weakly labeled data, as well as the road density in the areas chosen for training.
no code implementations • 16 Jul 2023 • Siwei Yang, Hanrong Ye, Dan Xu
A core objective in design is how to effectively model cross-task interactions to achieve a comprehensive improvement on different tasks based on their inherent complementarity and consistency.
no code implementations • 17 Apr 2023 • Bingchen Zhao, Jiahao Wang, Wufei Ma, Artur Jesslen, Siwei Yang, Shaozuo Yu, Oliver Zendel, Christian Theobalt, Alan Yuille, Adam Kortylewski
Enhancing the robustness of vision algorithms in real-world scenarios is challenging.
no code implementations • 7 Dec 2022 • Siwei Yang, Longlong Jing, Junfei Xiao, Hang Zhao, Alan Yuille, Yingwei Li
Through systematic analysis, we found that the commonly used pairwise affinity loss has two limitations: (1) it works with color affinity but leads to inferior performance with other modalities such as depth gradient, (2)the original affinity loss does not prevent trivial predictions as intended but actually accelerates this process due to the affinity loss term being symmetric.
1 code implementation • 3 Aug 2022 • Yixin Fei, Zhongkai Zhao, Siwei Yang, Bingchen Zhao
We address the problem of generalized category discovery (GCD) in this paper, i. e. clustering the unlabeled images leveraging the information from a set of seen classes, where the unlabeled images could contain both seen classes and unseen classes.
no code implementations • 28 Jun 2021 • Zihao Zhang, Shaozuo Yu, Siwei Yang, Yu Zhou, Bingchen Zhao
This paper presents the Rail-5k dataset for benchmarking the performance of visual algorithms in a real-world application scenario, namely the rail surface defects detection task.
1 code implementation • 6 Jun 2021 • Siwei Yang, Shaozuo Yu, Bingchen Zhao, Yin Wang
Visual pattern recognition over agricultural areas is an important application of aerial image processing.
1 code implementation • 21 Apr 2020 • Mang Tik Chiu, Xingqian Xu, Kai Wang, Jennifer Hobbs, Naira Hovakimyan, Thomas S. Huang, Honghui Shi, Yunchao Wei, Zilong Huang, Alexander Schwing, Robert Brunner, Ivan Dozier, Wyatt Dozier, Karen Ghandilyan, David Wilson, Hyunseong Park, Junhee Kim, Sungho Kim, Qinghui Liu, Michael C. Kampffmeyer, Robert Jenssen, Arnt B. Salberg, Alexandre Barbosa, Rodrigo Trevisan, Bingchen Zhao, Shaozuo Yu, Siwei Yang, Yin Wang, Hao Sheng, Xiao Chen, Jingyi Su, Ram Rajagopal, Andrew Ng, Van Thong Huynh, Soo-Hyung Kim, In-Seop Na, Ujjwal Baid, Shubham Innani, Prasad Dutande, Bhakti Baheti, Sanjay Talbar, Jianyu Tang
The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation task associated with our challenge dataset.