no code implementations • CVPR 2023 • Chen Lin, Bo Peng, Zheyang Li, Wenming Tan, Ye Ren, Jun Xiao, ShiLiang Pu
To this end, we detach a sharpness term from the loss which reflects the impact of quantization noise.
1 code implementation • ICCV 2023 • Liangqi Li, Jiaxu Miao, Dahu Shi, Wenming Tan, Ye Ren, Yi Yang, ShiLiang Pu
Current methods for open-vocabulary object detection (OVOD) rely on a pre-trained vision-language model (VLM) to acquire the recognition ability.
1 code implementation • ICCV 2023 • Heng Zhao, Shenxing Wei, Dahu Shi, Wenming Tan, Zheyang Li, Ye Ren, Xing Wei, Yi Yang, ShiLiang Pu
Taking the symmetry properties of objects into consideration, we design a symmetry-aware matching loss to facilitate the learning of dense point-wise geometry features and improve the performance considerably.
1 code implementation • NIPS 2022 • Zheng Chuanyang, Zheyang Li, Kai Zhang, Zhi Yang, Wenming Tan, Jun Xiao, Ye Ren, ShiLiang Pu
In this paper, we introduce joint importance, which integrates essential structural-aware interactions between components for the first time, to perform collaborative pruning.
1 code implementation • 2 Aug 2022 • Qiming Yang, Kai Zhang, Chaoxiang Lan, Zhi Yang, Zheyang Li, Wenming Tan, Jun Xiao, ShiLiang Pu
To tackle these issues, we propose Unified Normalization (UN), which can speed up the inference by being fused with other linear operations and achieve comparable performance on par with LN.
no code implementations • ACL 2022 • Mengze Li, Tianbao Wang, Haoyu Zhang, Shengyu Zhang, Zhou Zhao, Jiaxu Miao, Wenqiao Zhang, Wenming Tan, Jin Wang, Peng Wang, ShiLiang Pu, Fei Wu
To achieve effective grounding under a limited annotation budget, we investigate one-shot video grounding, and learn to ground natural language in all video frames with solely one frame labeled, in an end-to-end manner.
no code implementations • 17 Jan 2022 • Chen Lin, Zheyang Li, Bo Peng, Haoji Hu, Wenming Tan, Ye Ren, ShiLiang Pu
This paper introduces a post-training quantization~(PTQ) method achieving highly efficient Convolutional Neural Network~ (CNN) quantization with high performance.
1 code implementation • CVPR 2022 • Dahu Shi, Xing Wei, Liangqi Li, Ye Ren, Wenming Tan
Current methods of multi-person pose estimation typically treat the localization and association of body joints separately.
no code implementations • 31 Dec 2021 • Xing Wei, Yuanrui Kang, Jihao Yang, Yunfeng Qiu, Dahu Shi, Wenming Tan, Yihong Gong
First of all, we design a deformable attention in-built Transformer backbone, which learns adaptive feature representations with deformable sampling locations and dynamic attention weights.
1 code implementation • 21 Dec 2021 • Xiaodong Yu, Dahu Shi, Xing Wei, Ye Ren, Tingqun Ye, Wenming Tan
The pixel-wise mask, especially, is embedded by a group of parameters to construct a lightweight instance-aware transformer.
1 code implementation • 30 Aug 2021 • Gui-Song Xia, Jian Ding, Ming Qian, Nan Xue, Jiaming Han, Xiang Bai, Michael Ying Yang, Shengyang Li, Serge Belongie, Jiebo Luo, Mihai Datcu, Marcello Pelillo, Liangpei Zhang, Qiang Zhou, Chao-hui Yu, Kaixuan Hu, Yingjia Bu, Wenming Tan, Zhe Yang, Wei Li, Shang Liu, Jiaxuan Zhao, Tianzhi Ma, Zi-han Gao, Lingqi Wang, Yi Zuo, Licheng Jiao, Chang Meng, Hao Wang, Jiahao Wang, Yiming Hui, Zhuojun Dong, Jie Zhang, Qianyue Bao, Zixiao Zhang, Fang Liu
This report summarizes the results of Learning to Understand Aerial Images (LUAI) 2021 challenge held on ICCV 2021, which focuses on object detection and semantic segmentation in aerial images.
1 code implementation • 19 Jul 2021 • Dahu Shi, Xing Wei, Xiaodong Yu, Wenming Tan, Ye Ren, ShiLiang Pu
Multi-person pose estimation is an attractive and challenging task.
Ranked #4 on Multi-Person Pose Estimation on COCO minival
1 code implementation • 13 May 2021 • Hui Jiang, Yunlu Xu, Zhanzhan Cheng, ShiLiang Pu, Yi Niu, Wenqi Ren, Fei Wu, Wenming Tan
In this work, we excavate the implicit task, character counting within the traditional text recognition, without additional labor annotation cost.
1 code implementation • 13 May 2021 • Liang Qiao, Zaisheng Li, Zhanzhan Cheng, Peng Zhang, ShiLiang Pu, Yi Niu, Wenqi Ren, Wenming Tan, Fei Wu
In this paper, we aim to obtain more reliable aligned bounding boxes by fully utilizing the visual information from both text regions in proposed local features and cell relations in global features.
Ranked #8 on Table Recognition on PubTabNet
no code implementations • 1 Jan 2021 • Duo Li, Sanli Tang, Zhanzhan Cheng, ShiLiang Pu, Yi Niu, Wenming Tan, Fei Wu, Xiaokang Yang
However, the impact of the pseudo-labeled samples' quality as well as the mining strategies for high quality training sample have rarely been studied in SSL.
no code implementations • 17 Oct 2020 • Pengbo Zhao, Zhenshen Qu, Yingjia Bu, Wenming Tan, Ye Ren, ShiLiang Pu
Fast and precise object detection for high-resolution aerial images has been a challenging task over the years.
Ranked #35 on Object Detection In Aerial Images on DOTA (using extra training data)
no code implementations • 23 Sep 2020 • Zehan Zhang, Ming Zhang, Zhidong Liang, Xian Zhao, Ming Yang, Wenming Tan, ShiLiang Pu
Experimental results on the KITTI dataset demonstrate significant improvement in filtering false positive over the approach using only point cloud data.
no code implementations • ECCV 2018 • Bo Peng, Wenming Tan, Zheyang Li, Shun Zhang, Di Xie, ShiLiang Pu
In this paper we propose a novel decomposition method based on filter group approximation, which can significantly reduce the redundancy of deep convolutional neural networks (CNNs) while maintaining the majority of feature representation.