no code implementations • 6 Feb 2025 • Jinbo Xing, Long Mai, Cusuh Ham, Jiahui Huang, Aniruddha Mahapatra, Chi-Wing Fu, Tien-Tsin Wong, Feng Liu
This paper presents a method that allows users to design cinematic video shots in the context of image-to-video generation.
no code implementations • 23 Jan 2025 • Wailing Tang, Biqi Yang, Pheng-Ann Heng, Yun-hui Liu, Chi-Wing Fu
Few-shot Semantic Segmentation (FSS) is a challenging task that utilizes limited support images to segment associated unseen objects in query images.
1 code implementation • 24 Dec 2024 • Mengyang Wu, Yuzhi Zhao, Jialun Cao, Mingjie Xu, Zhongming Jiang, Xuehui Wang, Qinbin Li, GuangNeng Hu, Shengchao Qin, Chi-Wing Fu
Controversial contents largely inundate the Internet, infringing various cultural norms and child protection standards.
no code implementations • 3 Dec 2024 • Tianyu Wang, Jianming Zhang, Haitian Zheng, Zhihong Ding, Scott Cohen, Zhe Lin, Wei Xiong, Chi-Wing Fu, Luis Figueroa, Soo Ye Kim
MetaShadow combines the strengths of two cooperative components: Shadow Analyzer, for object-centered shadow detection and removal, and Shadow Synthesizer, for reference-based controllable shadow synthesis.
no code implementations • 2 Dec 2024 • Liqiang Lin, Wenpeng Wu, Chi-Wing Fu, Hao Zhang, Hui Huang
The optimized field, referred to as a feature volume, can be "probed" by the camera rays for novel view synthesis (NVS) and 3D geometry reconstruction.
1 code implementation • 14 Oct 2024 • Runsong Zhu, Shi Qiu, Qianyi Wu, Ka-Hei Hui, Pheng-Ann Heng, Chi-Wing Fu
Panoptic lifting is an effective technique to address the 3D panoptic segmentation task by unprojecting 2D panoptic segmentations from multi-views to 3D scene.
1 code implementation • 3 Sep 2024 • Xiaowei Hu, Zhenghao Xing, Tianyu Wang, Chi-Wing Fu, Pheng-Ann Heng
Shadows are formed when light encounters obstacles, leading to areas of diminished illumination.
1 code implementation • 3 Sep 2024 • Jiaqi Xu, Mengyang Wu, Xiaowei Hu, Chi-Wing Fu, Qi Dou, Pheng-Ann Heng
For clearness enhancement, we use real-world data, utilizing a dual-step strategy with pseudo-labels assessed by vision-language models and weather prompt learning.
1 code implementation • 19 Jul 2024 • Suyi Chen, Hao Xu, Haipeng Li, Kunming Luo, Guanghui Liu, Chi-Wing Fu, Ping Tan, Shuaicheng Liu
To enhance the data realism, we formulate a generative model as a depth inpainting diffusion to process the target depth map with the re-projected source depth map as the condition.
no code implementations • 11 Jun 2024 • Zhengzhe Liu, Qing Liu, Chirui Chang, Jianming Zhang, Daniil Pakhomov, Haitian Zheng, Zhe Lin, Daniel Cohen-Or, Chi-Wing Fu
Deoccluding the hidden portions of objects in a scene is a formidable task, particularly when addressing real-world scenes.
1 code implementation • CVPR 2024 • Hao Xu, Haipeng Li, Yinqiao Wang, Shuaicheng Liu, Chi-Wing Fu
Reconstructing 3D hand mesh robustly from a single image is very challenging, due to the lack of diversity in existing real-world datasets.
Ranked #4 on
3D Hand Pose Estimation
on HO-3D v2
no code implementations • 4 Feb 2024 • Jingyu Hu, Ka-Hei Hui, Zhengzhe Liu, Hao Zhang, Chi-Wing Fu
First, we design the coupled neural shape (CNS) representation for supporting 3D shape editing.
1 code implementation • 2 Feb 2024 • Yinqiao Wang, Hao Xu, Pheng-Ann Heng, Chi-Wing Fu
Estimating 3D hand mesh from RGB images is a longstanding track, in which occlusion is one of the most challenging problems.
1 code implementation • 20 Jan 2024 • Ka-Hei Hui, Aditya Sanghi, Arianna Rampini, Kamal Rahimi Malekshan, Zhengzhe Liu, Hooman Shayani, Chi-Wing Fu
Further, we derive the subband adaptive training strategy to train our model to effectively learn to generate coarse and detail wavelet coefficients.
no code implementations • 8 Nov 2023 • Biqi Yang, Weiliang Tang, Xiaojie Gao, Xianzhi Li, Yun-hui Liu, Chi-Wing Fu, Pheng-Ann Heng
In large-scale storehouses, precise instance masks are crucial for robotic bin picking but are challenging to obtain.
1 code implementation • 3 Nov 2023 • Zhengzhe Liu, Jingyu Hu, Ka-Hei Hui, Xiaojuan Qi, Daniel Cohen-Or, Chi-Wing Fu
This paper presents a new text-guided technique for generating 3D shapes.
1 code implementation • ICCV 2023 • Han Yang, Tianyu Wang, Xiaowei Hu, Chi-Wing Fu
Existing shadow detection datasets often contain missing or mislabeled shadows, which can hinder the performance of deep learning models trained directly on such data.
no code implementations • 14 Jun 2023 • Jingyu Hu, Ka-Hei Hui, Zhengzhe Liu, Hao Zhang, Chi-Wing Fu
This paper presents CLIPXPlore, a new framework that leverages a vision-language model to guide the exploration of the 3D shape space.
no code implementations • 4 May 2023 • Guoqing Yang, Fuyou Xue, Qi Zhang, Ke Xie, Chi-Wing Fu, Hui Huang
Besides, we propose B-Seg, a building instance segmentation method to establish UrbanBIS.
1 code implementation • 26 Mar 2023 • Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu
3D scene understanding, e. g., point cloud semantic and instance segmentation, often requires large-scale annotated training data, but clearly, point-wise labels are too tedious to prepare.
2 code implementations • 24 Mar 2023 • Zhengzhe Liu, Peng Dai, Ruihui Li, Xiaojuan Qi, Chi-Wing Fu
The core of our approach is a two-stage feature-space alignment strategy that leverages a pre-trained single-view reconstruction (SVR) model to map CLIP features to shapes: to begin with, map the CLIP image feature to the detail-rich 3D shape space of the SVR model, then map the CLIP text feature to the 3D shape space through encouraging the CLIP-consistency between rendered images and the input text.
no code implementations • 1 Feb 2023 • Jingyu Hu, Ka-Hei Hui, Zhengzhe Liu, Ruihui Li, Chi-Wing Fu
This paper presents a new approach for 3D shape generation, inversion, and manipulation, through a direct generative modeling on a continuous implicit representation in wavelet domain.
no code implementations • ICCV 2023 • Fanbin Lu, Xufeng Yao, Chi-Wing Fu, Jiaya Jia
Our denoising model outperforms the state-of-the-art reconstruction-based anomaly detection methods for precise anomaly localization and high-quality normal image reconstruction on the MVTec-AD benchmark.
1 code implementation • ICCV 2023 • Suyi Chen, Hao Xu, Ru Li, Guanghui Liu, Chi-Wing Fu, Shuaicheng Liu
We design SIRA-PCR, a new approach to 3D point cloud registration.
no code implementations • CVPR 2023 • Ruihang Chu, Zhengzhe Liu, Xiaoqing Ye, Xiao Tan, Xiaojuan Qi, Chi-Wing Fu, Jiaya Jia
The key of Cart is to utilize the prediction of object structures to connect visual observations with user commands for effective manipulations.
1 code implementation • CVPR 2023 • Hao Xu, Tianyu Wang, Xiao Tang, Chi-Wing Fu
First, we decouple hand mesh reconstruction into two branches, one to exploit finger-level non-occluded information and the other to exploit global hand orientation, with lightweight structures to promote real-time inference.
Ranked #5 on
3D Hand Pose Estimation
on DexYCB
1 code implementation • 23 Nov 2022 • Zhenghao Xing, Tianyu Wang, Xiaowei Hu, Haoran Wu, Chi-Wing Fu, Pheng-Ann Heng
Instance shadow detection, crucial for applications such as photo editing and light direction estimation, has undergone significant advancements in predicting shadow instances, object instances, and their associations.
1 code implementation • 23 Nov 2022 • Tianyu Wang, Xiaowei Hu, Zhengzhe Liu, Chi-Wing Fu
Importantly, we formulate the lightweight plug-in S2D module and the point cloud reconstruction module in SDet to densify 3D features and train SDet to produce 3D features, following the dense 3D features in DDet.
no code implementations • 21 Sep 2022 • Yilin Liu, Liqiang Lin, Yue Hu, Ke Xie, Chi-Wing Fu, Hao Zhang, Hui Huang
To reconstruct a new urban scene, we first build the 3D scene proxy, then rely on the predicted reconstruction quality and uncertainty measures by our network, based off of the proxy geometry, to guide the drone path planning.
1 code implementation • 19 Sep 2022 • Ka-Hei Hui, Ruihui Li, Jingyu Hu, Chi-Wing Fu
This paper presents a new approach for 3D shape generation, enabling direct generative modeling on a continuous implicit representation in wavelet domain.
2 code implementations • 9 Sep 2022 • Zhengzhe Liu, Peng Dai, Ruihui Li, Xiaojuan Qi, Chi-Wing Fu
Text-guided 3D shape generation remains challenging due to the absence of large paired text-shape data, the substantial semantic gap between these two modalities, and the structural complexity of 3D shapes.
2 code implementations • 11 Jul 2022 • Tianyu Wang, Xiaowei Hu, Pheng-Ann Heng, Chi-Wing Fu
This paper formulates a new problem, instance shadow detection, which aims to detect shadow instance and the associated object instance that cast each shadow in the input image.
Ranked #1 on
Instance Shadow Detection
on SOBA
1 code implementation • 5 Jul 2022 • Xiaogang Xu, RuiXing Wang, Chi-Wing Fu, Jiaya Jia
Despite the quality improvement brought by the recent methods, video super-resolution (SR) is still very challenging, especially for videos that are low-light and noisy.
no code implementations • 3 Jul 2022 • Wu Zheng, Li Jiang, Fanbin Lu, Yangyang Ye, Chi-Wing Fu
To boost a detector for single-frame 3D object detection, we present a new approach to train it to simulate features and responses following a detector trained on multi-frame point clouds.
no code implementations • CVPR 2022 • Wu Zheng, Mingxuan Hong, Li Jiang, Chi-Wing Fu
This paper presents a new approach to boost a single-modality (LiDAR) 3D object detector by teaching it to simulate features and responses that follow a multi-modality (LiDAR-image) detector.
no code implementations • 14 Jun 2022 • Runsong Zhu, Di Kang, Ka-Hei Hui, Yue Qian, Xuefei Zhe, Zhen Dong, Linchao Bao, Pheng-Ann Heng, Chi-Wing Fu
To guide the network quickly fit the coarse shape, we propose to utilize the signed supervision in regions that are obviously outside the object and can be easily determined, resulting in our semi-signed supervision.
1 code implementation • CVPR 2022 • Ka-Hei Hui, Ruihui Li, Jingyu Hu, Chi-Wing Fu
This paper introduces a novel framework called DTNet for 3D mesh reconstruction and generation via Disentangled Topology.
1 code implementation • CVPR 2022 • Zhengzhe Liu, Yi Wang, Xiaojuan Qi, Chi-Wing Fu
In this work, we explore the challenging task of generating 3D shapes from text.
no code implementations • 5 Mar 2022 • Yidan Feng, Biqi Yang, Xianzhi Li, Chi-Wing Fu, Rui Cao, Kai Chen, Qi Dou, Mingqiang Wei, Yun-hui Liu, Pheng-Ann Heng
Industrial bin picking is a challenging task that requires accurate and robust segmentation of individual object instances.
1 code implementation • 28 Feb 2022 • Ruihui Li, Xianzhi Li, Tien-Tsin Wong, Chi-Wing Fu
To achieve a learnable self-embedding scheme, we design a novel framework with two jointly-trained networks: one to encode the input point set into its self-embedded sparse point set and the other to leverage the embedded information for inverting the original point set back.
no code implementations • CVPR 2022 • Ruihang Chu, Xiaoqing Ye, Zhengzhe Liu, Xiao Tan, Xiaojuan Qi, Chi-Wing Fu, Jiaya Jia
We explore the way to alleviate the label-hungry problem in a semi-supervised setting for 3D instance segmentation.
1 code implementation • CVPR 2022 • Xiaogang Xu, RuiXing Wang, Chi-Wing Fu, Jiaya Jia
They are long-range operations for image regions of extremely low Signal-to-Noise-Ratio (SNR) and short-range operations for other regions.
Ranked #3 on
Low-Light Image Enhancement
on LIME
2 code implementations • ICCV 2021 • Li Jiang, Shaoshuai Shi, Zhuotao Tian, Xin Lai, Shu Liu, Chi-Wing Fu, Jiaya Jia
To address the high cost and challenges of 3D point-level labeling, we present a method for semi-supervised point cloud semantic segmentation to adopt unlabeled point clouds in training to boost the model performance.
no code implementations • ICCV 2021 • Xiao Tang, Tianyu Wang, Chi-Wing Fu
3D hand-mesh reconstruction from RGB images facilitates many applications, including augmented reality (AR).
Ranked #18 on
3D Hand Pose Estimation
on FreiHAND
1 code implementation • 10 Aug 2021 • Ruihui Li, Xianzhi Li, Ka-Hei Hui, Chi-Wing Fu
We present SP-GAN, a new unsupervised sphere-guided generative model for direct synthesis of 3D shapes in the form of point clouds.
1 code implementation • 28 Jul 2021 • Xiaojie Gao, Yueming Jin, Qi Dou, Chi-Wing Fu, Pheng-Ann Heng
Video prediction methods generally consume substantial computing resources in training and deployment, among which keypoint-based approaches show promising improvement in efficiency by simplifying dense image prediction to light keypoint prediction.
Ranked #1 on
Video Prediction
on KTH
no code implementations • 6 Jul 2021 • Mengyang Wu, Wei Zeng, Chi-Wing Fu
The ability to recognize the position and order of the floor-level lines that divide adjacent building floors can benefit many applications, for example, urban augmented reality (AR).
1 code implementation • CVPR 2021 • Tianyu Wang, Xiaowei Hu, Chi-Wing Fu, Pheng-Ann Heng
Instance shadow detection aims to find shadow instances paired with the objects that cast the shadows.
Ranked #2 on
Instance Shadow Detection
on SOBA
3 code implementations • CVPR 2021 • Ruihui Li, Xianzhi Li, Pheng-Ann Heng, Chi-Wing Fu
Point clouds produced by 3D scanning are often sparse, non-uniform, and noisy.
1 code implementation • CVPR 2021 • Wu Zheng, Weiliang Tang, Li Jiang, Chi-Wing Fu
Lastly, to better exploit hard targets, we design an ODIoU loss to supervise the student with constraints on the predicted box centers and orientations.
Ranked #1 on
Birds Eye View Object Detection
on KITTI Cars Easy
1 code implementation • CVPR 2021 • Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu
First, we distill 3D knowledge from a pretrained 3D network to supervise a 2D network to learn simulated 3D features from 2D features during the training, so the 2D network can infer without requiring 3D data.
2 code implementations • CVPR 2021 • Zhengzhe Liu, Xiaojuan Qi, Chi-Wing Fu
Point cloud semantic segmentation often requires largescale annotated training data, but clearly, point-wise labels are too tedious to prepare.
1 code implementation • ICCV 2021 • RuiXing Wang, Xiaogang Xu, Chi-Wing Fu, Jiangbo Lu, Bei Yu, Jiaya Jia
Low-light video enhancement is an important task.
no code implementations • 11 Dec 2020 • Liqiang Lin, Pengdi Huang, Chi-Wing Fu, Kai Xu, Hao Zhang, Hui Huang
We present a novel attention-based mechanism to learn enhanced point features for point cloud processing tasks, e. g., classification and segmentation.
1 code implementation • 5 Dec 2020 • Wu Zheng, Weiliang Tang, Sijin Chen, Li Jiang, Chi-Wing Fu
Existing single-stage detectors for locating objects in point clouds often treat object localization and category classification as separate tasks, so the localization accuracy and classification confidence may not well align.
Ranked #3 on
Birds Eye View Object Detection
on KITTI Cars Easy
1 code implementation • 13 Oct 2020 • Shujun Wang, Lequan Yu, Kang Li, Xin Yang, Chi-Wing Fu, Pheng-Ann Heng
Our DoFE framework dynamically enriches the image features with additional domain prior knowledge learned from multi-source domains to make the semantic features more discriminative.
1 code implementation • 3 Sep 2020 • Wenbo Hu, Menghan Xia, Chi-Wing Fu, Tien-Tsin Wong
This paper presents the idea ofmono-nizingbinocular videos and a frame-work to effectively realize it.
Image and Video Processing Graphics
no code implementations • ECCV 2020 • Shujun Wang, Lequan Yu, Caizi Li, Chi-Wing Fu, Pheng-Ann Heng
To this end, we present a new domain generalization framework that learns how to generalize across domains simultaneously from extrinsic relationship supervision and intrinsic self-supervision for images from multi-source domains.
1 code implementation • 5 Jul 2020 • Hao Xu, Ka Hei Hui, Chi-Wing Fu, Hao Zhang
To start, we reformulate tiling as a graph problem by modeling candidate tile locations in the target shape as graph nodes and connectivity between tile locations as edges.
2 code implementations • CVPR 2020 • Li Jiang, Hengshuang Zhao, Shaoshuai Shi, Shu Liu, Chi-Wing Fu, Jiaya Jia
Instance segmentation is an important task for scene understanding.
Ranked #9 on
3D Instance Segmentation
on STPLS3D
1 code implementation • 16 Mar 2020 • Xianzhi Li, Ruihui Li, Guangyong Chen, Chi-Wing Fu, Daniel Cohen-Or, Pheng-Ann Heng
Recently, many deep neural networks were designed to process 3D point clouds, but a common drawback is that rotation invariance is not ensured, leading to poor generalization to arbitrary orientations.
no code implementations • 14 Mar 2020 • Chao Huang, Ruihui Li, Xianzhi Li, Chi-Wing Fu
This paper presents a novel non-local part-aware deep neural network to denoise point clouds by exploring the inherent non-local self-similarity in 3D objects and scenes.
2 code implementations • CVPR 2020 • Ruihui Li, Xianzhi Li, Pheng-Ann Heng, Chi-Wing Fu
We present PointAugment, a new auto-augmentation framework that automatically optimizes and augments point cloud samples to enrich the data diversity when we train a classification network.
Ranked #2 on
3D Point Cloud Data Augmentation
on ModelNet40
3 code implementations • CVPR 2020 • Tianyu Wang, Xiao-Wei Hu, Qiong Wang, Pheng-Ann Heng, Chi-Wing Fu
Then, we pair up the predicted shadow and object instances, and match them with the predicted shadow-object associations to generate the final results.
Ranked #3 on
Instance Shadow Detection
on SOBA
2 code implementations • 16 Nov 2019 • Xiaowei Hu, Tianyu Wang, Chi-Wing Fu, Yitong Jiang, Qiong Wang, Pheng-Ann Heng
Shadow detection in general photos is a nontrivial problem, due to the complexity of the real world.
Ranked #10 on
Shadow Detection
on CUHK-Shadow
1 code implementation • 4 Nov 2019 • Xiaomeng Li, Xiao-Wei Hu, Lequan Yu, Lei Zhu, Chi-Wing Fu, Pheng-Ann Heng
In this paper, we present a novel cross-disease attention network (CANet) to jointly grade DR and DME by exploring the internal relationship between the diseases with only image-level supervision.
no code implementations • ICCV 2019 • Li Jiang, Hengshuang Zhao, Shu Liu, Xiaoyong Shen, Chi-Wing Fu, Jiaya Jia
To incorporate point features in the edge branch, we establish a hierarchical graph framework, where the graph is initialized from a coarse layer and gradually enriched along the point decoding process.
Ranked #46 on
Semantic Segmentation
on S3DIS Area5
3 code implementations • ICCV 2019 • Zhiliang Zeng, Xianzhi Li, Ying Kin Yu, Chi-Wing Fu
Besides walls and rooms, we aim to recognize diverse floor plan elements, such as doors, windows and different types of rooms, in the floor layouts.
no code implementations • 31 Jul 2019 • Zhutian Chen, Wei Zeng, Zhiguang Yang, Lingyun Yu, Chi-Wing Fu, Huamin Qu
A hierarchical network is trained using a dataset with over 30K lasso-selection records on two different point cloud data.
Human-Computer Interaction Graphics
3 code implementations • ICCV 2019 • Ruihui Li, Xianzhi Li, Chi-Wing Fu, Daniel Cohen-Or, Pheng-Ann Heng
Point clouds acquired from range scans are often sparse, noisy, and non-uniform.
8 code implementations • 16 Jul 2019 • Lequan Yu, Shujun Wang, Xiaomeng Li, Chi-Wing Fu, Pheng-Ann Heng
We design a novel uncertainty-aware scheme to enable the student model to gradually learn from the meaningful and reliable targets by exploiting the uncertainty information.
1 code implementation • 13 Jul 2019 • Yueming Jin, Huaxia Li, Qi Dou, Hao Chen, Jing Qin, Chi-Wing Fu, Pheng-Ann Heng
Mutually leveraging both low-level feature sharing and high-level prediction correlating, our MTRCNet-CL method can encourage the interactions between the two tasks to a large extent, and hence can bring about benefits to each other.
Ranked #3 on
Surgical tool detection
on Cholec80
no code implementations • 6 Jul 2019 • Xiaomeng Li, Lequan Yu, Chi-Wing Fu, Meng Fang, Pheng-Ann Heng
However, the importance of feature embedding, i. e., exploring the relationship among training samples, is neglected.
no code implementations • 30 Jun 2019 • Xiaomeng Li, Lequan Yu, Yueming Jin, Chi-Wing Fu, Lei Xing, Pheng-Ann Heng
Rare diseases have extremely low-data regimes, unlike common diseases with large amount of available labeled data.
1 code implementation • 26 Jun 2019 • Shujun Wang, Lequan Yu, Kang Li, Xin Yang, Chi-Wing Fu, Pheng-Ann Heng
The cross-domain discrepancy (domain shift) hinders the generalization of deep neural networks to work on different domain datasets. In this work, we present an unsupervised domain adaptation framework, called Boundary and Entropy-driven Adversarial Learning (BEAL), to improve the OD and OC segmentation performance, especially on the ambiguous boundary regions.
no code implementations • 5 May 2019 • Xianzhi Li, Lequan Yu, Chi-Wing Fu, Daniel Cohen-Or, Pheng-Ann Heng
This paper presents a novel approach to learn and detect distinctive regions on 3D shapes.
5 code implementations • ICCV 2019 • Xiaowei Hu, Yitong Jiang, Chi-Wing Fu, Pheng-Ann Heng
This paper presents a new method for shadow removal using unpaired data, enabling us to avoid tedious annotations and obtain more diverse training samples.
Ranked #4 on
Shadow Removal
on SRD
no code implementations • 25 Mar 2019 • Xiaowei Hu, Chi-Wing Fu, Lei Zhu, Tianyu Wang, Pheng-Ann Heng
This paper presents a new deep neural network design for salient object detection by maximizing the integration of local and global image context within, around, and beyond the salient objects.
no code implementations • 28 Feb 2019 • Xiaomeng Li, Lequan Yu, Hao Chen, Chi-Wing Fu, Lei Xing, Pheng-Ann Heng
In this paper, we present a novel semi-supervised method for medical image segmentation, where the network is optimized by the weighted combination of a common supervised loss for labeled inputs only and a regularization loss for both labeled and unlabeled data.
no code implementations • 20 Feb 2019 • Shujun Wang, Lequan Yu, Xin Yang, Chi-Wing Fu, Pheng-Ann Heng
In this paper, we present a novel patchbased Output Space Adversarial Learning framework (pOSAL) to jointly and robustly segment the OD and OC from different fundus image datasets.
Ranked #2 on
Optic Disc Segmentation
on REFUGE
6 code implementations • 13 Jan 2019 • Patrick Bilic, Patrick Christ, Hongwei Bran Li, Eugene Vorontsov, Avi Ben-Cohen, Georgios Kaissis, Adi Szeskin, Colin Jacobs, Gabriel Efrain Humpire Mamani, Gabriel Chartrand, Fabian Lohöfer, Julian Walter Holch, Wieland Sommer, Felix Hofmann, Alexandre Hostettler, Naama Lev-Cohain, Michal Drozdzal, Michal Marianne Amitai, Refael Vivantik, Jacob Sosna, Ivan Ezhov, Anjany Sekuboyina, Fernando Navarro, Florian Kofler, Johannes C. Paetzold, Suprosanna Shit, Xiaobin Hu, Jana Lipková, Markus Rempfler, Marie Piraud, Jan Kirschke, Benedikt Wiestler, Zhiheng Zhang, Christian Hülsemeyer, Marcel Beetz, Florian Ettlinger, Michela Antonelli, Woong Bae, Míriam Bellver, Lei Bi, Hao Chen, Grzegorz Chlebus, Erik B. Dam, Qi Dou, Chi-Wing Fu, Bogdan Georgescu, Xavier Giró-i-Nieto, Felix Gruen, Xu Han, Pheng-Ann Heng, Jürgen Hesser, Jan Hendrik Moltz, Christian Igel, Fabian Isensee, Paul Jäger, Fucang Jia, Krishna Chaitanya Kaluva, Mahendra Khened, Ildoo Kim, Jae-Hun Kim, Sungwoong Kim, Simon Kohl, Tomasz Konopczynski, Avinash Kori, Ganapathy Krishnamurthi, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Li, John Lowengrub, Jun Ma, Klaus Maier-Hein, Kevis-Kokitsi Maninis, Hans Meine, Dorit Merhof, Akshay Pai, Mathias Perslev, Jens Petersen, Jordi Pont-Tuset, Jin Qi, Xiaojuan Qi, Oliver Rippel, Karsten Roth, Ignacio Sarasua, Andrea Schenk, Zengming Shen, Jordi Torres, Christian Wachinger, Chunliang Wang, Leon Weninger, Jianrong Wu, Daguang Xu, Xiaoping Yang, Simon Chun-Ho Yu, Yading Yuan, Miao Yu, Liping Zhang, Jorge Cardoso, Spyridon Bakas, Rickmer Braren, Volker Heinemann, Christopher Pal, An Tang, Samuel Kadoury, Luc Soler, Bram van Ginneken, Hayit Greenspan, Leo Joskowicz, Bjoern Menze
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
1 code implementation • ECCV 2018 • Lei Zhu, Zijun Deng, Xiao-Wei Hu, Chi-Wing Fu, Xuemiao Xu, Jing Qin, Pheng-Ann Heng
Second, we develop a bidirectional feature pyramid network (BFPN) to aggregate shadow contexts spanned across different CNN layers by deploying two series of RAR modules in the network to iteratively combine and refine context features: one series to refine context features from deep to shallow layers, and another series from shallow to deep layers.
Ranked #6 on
Shadow Detection
on SBU / SBU-Refine
no code implementations • 12 Aug 2018 • Xiaomeng Li, Lequan Yu, Hao Chen, Chi-Wing Fu, Pheng-Ann Heng
In this paper, we present a novel semi-supervised method for skin lesion segmentation, where the network is optimized by the weighted combination of a common supervised loss for labeled inputs only and a regularization loss for both labeled and unlabeled data.
no code implementations • ECCV 2018 • Lequan Yu, Xianzhi Li, Chi-Wing Fu, Daniel Cohen-Or, Pheng-Ann Heng
In this paper, we present the first deep learning based edge-aware technique to facilitate the consolidation of point clouds.
1 code implementation • 8 Jul 2018 • Xiaomeng Li, Lequan Yu, Chi-Wing Fu, Pheng-Ann Heng
Our best model achieves 77. 23\%(JA) on the test dataset, outperforming the state-of-the-art challenging methods and further demonstrating the effectiveness of our proposed deeply supervised rotation equivariant segmentation network.
2 code implementations • 12 May 2018 • Xiaowei Hu, Chi-Wing Fu, Lei Zhu, Jing Qin, Pheng-Ann Heng
This paper presents a novel deep neural network design for shadow detection and removal by analyzing the spatial image context in a direction-aware manner.
Ranked #6 on
Shadow Removal
on SRD
3 code implementations • CVPR 2018 • Lequan Yu, Xianzhi Li, Chi-Wing Fu, Daniel Cohen-Or, Pheng-Ann Heng
Learning and analyzing 3D point clouds with deep networks is challenging due to the sparseness and irregularity of the data.
Ranked #3 on
Point Cloud Super Resolution
on SHREC15
2 code implementations • CVPR 2018 • Xiaowei Hu, Lei Zhu, Chi-Wing Fu, Jing Qin, Pheng-Ann Heng
To achieve this, we first formulate the direction-aware attention mechanism in a spatial recurrent neural network (RNN) by introducing attention weights when aggregating spatial context features in the RNN.
Ranked #2 on
RGB Salient Object Detection
on SBU / SBU-Refine
no code implementations • ICCV 2017 • Lei Zhu, Chi-Wing Fu, Dani Lischinski, Pheng-Ann Heng
A third prior is defined on the rain-streak layer R, based on similarity of patches to the extracted rain patches.
2 code implementations • 21 Sep 2017 • Xiaomeng Li, Hao Chen, Xiaojuan Qi, Qi Dou, Chi-Wing Fu, Pheng Ann Heng
Our method outperformed other state-of-the-arts on the segmentation results of tumors and achieved very competitive performance for liver segmentation even with a single model.
Ranked #1 on
Liver Segmentation
on LiTS2017
(Dice metric)
Automatic Liver And Tumor Segmentation
Image Segmentation
+4
no code implementations • CVPR 2017 • Lei Zhu, Chi-Wing Fu, Michael S. Brown, Pheng-Ann Heng
`Speckle' refers to the granular patterns that occur in ultrasound images due to wave interference.