no code implementations • 12 Dec 2022 • Zhiwei Lin, Yongtao Wang
Besides, we introduce a learnable point token to maintain a consistent receptive field size of the 3D encoder with fine-tuning for masked point cloud inputs.
1 code implementation • 16 Nov 2022 • Hao Huang, Ziyan Chen, Huanran Chen, Yongtao Wang, Kevin Zhang
Then, we analogize patch optimization with regular model optimization, proposing a series of self-ensemble approaches on the input data, the attacked model, and the adversarial patch to efficiently make use of the limited information and prevent the patch from overfitting.
1 code implementation • 24 Oct 2022 • Zhiwei Lin, Zengyu Yang, Yongtao Wang
Firstly, we present a foreground guidance strategy with an off-the-shelf UOD detector to highlight the foreground regions on the feature maps and then refine object locations in an iterative fashion.
no code implementations • 18 Aug 2022 • Xuanyang Zhang, Yonggang Li, Xiangyu Zhang, Yongtao Wang, Jian Sun
Differentiable architecture search (DARTS) has significantly promoted the development of NAS techniques because of its high search efficiency and effectiveness but suffers from performance collapse.
Ranked #11 on Neural Architecture Search on NAS-Bench-201, CIFAR-10
1 code implementation • 4 Jul 2022 • Zhiwei Lin, TingTing Liang, Taihong Xiao, Yongtao Wang, Zhi Tang, Ming-Hsuan Yang
To address this issue, we propose a neural architecture search method named FlowNAS to automatically find the better encoder architecture for flow estimation task.
1 code implementation • 30 May 2022 • Kaicheng Yu, Tang Tao, Hongwei Xie, Zhiwei Lin, Zhongwei Wu, Zhongyu Xia, TingTing Liang, Haiyang Sun, Jiong Deng, Dayang Hao, Yongtao Wang, Xiaodan Liang, Bing Wang
There are two critical sensors for 3D perception in autonomous driving, the camera and the LiDAR.
no code implementations • 27 May 2022 • TingTing Liang, Hongwei Xie, Kaicheng Yu, Zhongyu Xia, Zhiwei Lin, Yongtao Wang, Tao Tang, Bing Wang, Zhi Tang
Fusing the camera and LiDAR information has become a de-facto standard for 3D object detection tasks.
1 code implementation • 6 Apr 2022 • Xiaojie Chu, Yongtao Wang
By combining the proposed IterVM with iterative language modeling module, we further propose a powerful scene text recognizer called IterNet.
2 code implementations • 13 Mar 2022 • Xiaojie Chu, Yongtao Wang, Chunhua Shen, Jingdong Chen, Wei Chu
The development of scene text recognition (STR) in the era of deep learning has been mainly focused on novel architectures of STR models.
1 code implementation • 5 Jul 2021 • Zhiwei Lin, Yongtao Wang, Hongxiang Lin
In this paper, we make the first attempt to tackle the catastrophic forgetting problem in the mainstream self-supervised methods, i. e., contrastive learning methods.
5 code implementations • 1 Jul 2021 • TingTing Liang, Xiaojie Chu, Yudong Liu, Yongtao Wang, Zhi Tang, Wei Chu, Jingdong Chen, Haibin Ling
With multi-scale testing, we push the current best single model result to a new record of 60. 1% box AP and 52. 3% mask AP without using extra training data.
Ranked #11 on Instance Segmentation on COCO test-dev
1 code implementation • 23 May 2021 • Hao Huang, Yongtao Wang, Zhaoyu Chen, Yuze Zhang, Yuheng Li, Zhi Tang, Wei Chu, Jingdong Chen, Weisi Lin, Kai-Kuang Ma
Then, we design a two-level perturbation fusion strategy to alleviate the conflict between the adversarial watermarks generated by different facial images and models.
1 code implementation • 23 Mar 2021 • Hao Huang, Yongtao Wang, Zhaoyu Chen, Zhi Tang, Wenqiang Zhang, Kai-Kuang Ma
Firstly, we propose a patch selection and refining scheme to find the pixels which have the greatest importance for attack and remove the inconsequential perturbations gradually.
1 code implementation • CVPR 2021 • TingTing Liang, Yongtao Wang, Zhi Tang, Guosheng Hu, Haibin Ling
Encouraged by the success, we propose a novel One-Shot Path Aggregation Network Architecture Search (OPANAS) algorithm, which significantly improves both searching efficiency and detection accuracy.
1 code implementation • 15 Sep 2020 • Jianwei Li, Yongtao Wang, Haihua Xie, Kai-Kuang Ma
Our proposed network is a single model approach that can be trained for handling a wide range of quality factors while consistently delivering superior or comparable image artifacts removal performance.
1 code implementation • 27 May 2020 • Zhuoying Wang, Yongtao Wang, Zhi Tang, Yangyan Li, Ying Chen, Haibin Ling, Weisi Lin
Existing CNN-based methods for pixel labeling heavily depend on multi-scale features to meet the requirements of both semantic comprehension and detail preservation.
1 code implementation • ECCV 2020 • Yonggang Li, Guosheng Hu, Yongtao Wang, Timothy Hospedales, Neil M. Robertson, Yongxin Yang
In this paper, we propose Differentiable Automatic Data Augmentation (DADA) which dramatically reduces the cost.
Ranked #11 on Data Augmentation on ImageNet
no code implementations • 19 Jan 2020 • Kaiyu Shan, Yongtao Wang, Zhuoying Wang, TingTing Liang, Zhi Tang, Ying Chen, Yangyan Li
To efficiently extract spatiotemporal features of video for action recognition, most state-of-the-art methods integrate 1D temporal convolution into a conventional 2D CNN backbone.
no code implementations • 20 Dec 2019 • Ting-Ting Liang, Yongtao Wang, Qijie Zhao, huan zhang, Zhi Tang, Haibin Ling
Feature pyramids are widely exploited in many detectors to solve the scale variation problem for object detection.
6 code implementations • 9 Sep 2019 • Yudong Liu, Yongtao Wang, Siwei Wang, Ting-Ting Liang, Qijie Zhao, Zhi Tang, Haibin Ling
In existing CNN based detectors, the backbone network is a very important component for basic feature extraction, and the performance of the detectors highly depends on it.
Ranked #38 on Instance Segmentation on COCO test-dev
7 code implementations • 12 Nov 2018 • Qijie Zhao, Tao Sheng, Yongtao Wang, Zhi Tang, Ying Chen, Ling Cai, Haibin Ling
Finally, we gather up the decoder layers with equivalent scales (sizes) to develop a feature pyramid for object detection, in which every feature map consists of the layers (features) from multiple levels.
Ranked #146 on Object Detection on COCO test-dev
no code implementations • 31 Jul 2018 • Qijie Zhao, Feng Ni, Yang song, Yongtao Wang, Zhi Tang
Specifically, a synthesizing method was proposed to generate well-annotated images containing barcode and QR code labels, which contributes to largely decrease the annotation time.
1 code implementation • 26 Jun 2018 • Qijie Zhao, Tao Sheng, Yongtao Wang, Feng Ni, Ling Cai
The ability to detect small objects and the speed of the object detector are very important for the application of autonomous driving, and in this paper, we propose an effective yet efficient one-stage detector, which gained the second place in the Road Object Detection competition of CVPR2018 workshop - Workshop of Autonomous Driving(WAD).
no code implementations • ICCV 2017 • Yuan Liao, Xiaoqing Lu, Chengcui Zhang, Yongtao Wang, Zhi Tang
Mutual enhancement is also included in our frame propagation mechanism that improves logo detection by utilizing the continuity of logos across frames.