In this paper, we introduce a brand new dataset to promote the study of instance segmentation for objects with irregular shapes.
Ranked #1 on Instance Segmentation on iShape
no code implementations • 7 Jun 2021 • Goutam Bhat, Martin Danelljan, Radu Timofte, Kazutoshi Akita, Wooyeong Cho, Haoqiang Fan, Lanpeng Jia, Daeshik Kim, Bruno Lecouat, Youwei Li, Shuaicheng Liu, Ziluan Liu, Ziwei Luo, Takahiro Maeda, Julien Mairal, Christian Micheloni, Xuan Mo, Takeru Oba, Pavel Ostyakov, Jean Ponce, Sanghyeok Son, Jian Sun, Norimichi Ukita, Rao Muhammad Umer, Youliang Yan, Lei Yu, Magauiya Zhussip, Xueyi Zou
This paper reviews the NTIRE2021 challenge on burst super-resolution.
In this paper, we present an attention-guided deformable convolutional network for hand-held multi-frame high dynamic range (HDR) imaging, namely ADNet.
no code implementations • 17 May 2021 • Andrey Ignatov, Kim Byeoung-su, Radu Timofte, Angeline Pouget, Fenglong Song, Cheng Li, Shuai Xiao, Zhongqian Fu, Matteo Maggioni, Yibin Huang, Shen Cheng, Xin Lu, Yifeng Zhou, Liangyu Chen, Donghao Liu, Xiangyu Zhang, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Minsu Kwon, Myungje Lee, Jaeyoon Yoo, Changbeom Kang, Shinjo Wang, Bin Huang, Tianbao Zhou, Shuai Liu, Lei Lei, Chaoyu Feng, Liguang Huang, Zhikun Lei, Feifei Chen
A detailed description of all models developed in the challenge is provided in this paper.
Equipped with these two modules, our method achieves the best performance for unsupervised optical flow estimation on multiple leading benchmarks, including MPI-SIntel, KITTI 2012 and KITTI 2015.
Last, we propose a Feature Identity Loss (FIL) to enforce the learned image feature warp-equivariant, meaning that the result should be identical if the order of warp operation and feature extraction is swapped.
Rethinking both, we learn the distribution of underlying high-frequency details in a discrete form and propose a two-stage pipeline: divergence stage to convergence stage.
Moreover, at the output representation stage, we designed a simple but effective 3D keypoints selection algorithm considering the texture and geometry information of objects, which simplifies keypoint localization for precise pose estimation.
Subsequently, image denosing can be achieved by selecting corresponding basis of the signal subspace and projecting the input into such space.
Ranked #2 on Image Denoising on DND
By integrating these two components together, our method achieves the best performance for unsupervised optical flow learning on multiple leading benchmarks, including MPI-SIntel, KITTI 2012 and KITTI 2015.
Ranked #1 on Optical Flow Estimation on KITTI 2012 unsupervised
Our method is a natural extension of 2D-keypoint approaches that successfully work on RGB based 6DoF estimation.
Ranked #1 on 6D Pose Estimation on YCB-Video
Most previous image matting methods require a roughly-specificed trimap as input, and estimate fractional alpha values for all pixels that are in the unknown region of the trimap.
In this paper, we present new data pre-processing and augmentation techniques for DNN-based raw image denoising.
The fusion block not only provides a smooth fusion between restored and existing content, but also provides an attention map to make network focus more on the unknown pixels.
This paper introduces an extremely efficient CNN architecture named DFANet for semantic segmentation under resource constraints.
Ranked #9 on Semantic Segmentation on CamVid
Our final solution is a conditional shape sampler, capable of predicting multiple plausible 3D point clouds from an input image.
Ranked #2 on 3D Reconstruction on Data3D−R2N2 (using extra training data)
Our basic network is capable of achieving high recognition accuracy ($85. 8\%$ on LFW benchmark) with only 8 dimension representation.