801 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- ETH ZurichCH39 papers
- Chinese University of Hong KongHK6 papers
- KU LeuvenBE6 papers
- Shenzhen Institutes of Advanced TechnologyCN5 papers
- Chinese Academy of SciencesCN4 papers
- Imperial College LondonGB3 papers
- Johns Hopkins UniversityUS3 papers
- Nanjing UniversityCN2 papers
- Technical University of MunichDE2 papers
- University of California, IrvineUS2 papers
- University of Chinese Academy of SciencesCN2 papers
- University of Electronic Science and Technology of ChinaCN2 papers
34 papers · 1 filter
Deep Gradient Learning for Efficient Camouflaged Object Detection
Ge-Peng Ji, Deng-Ping Fan, Yu-Cheng Chou +3
This paper introduces DGNet, a novel deep framework that exploits object gradient supervision for camouflaged object detection (COD). It decouples the task into two connected branc…
Facial-Sketch Synthesis: A New Challenge
Deng-Ping Fan, Ziling Huang, Peng Zheng +3
This paper aims to conduct a comprehensive study on facial-sketch synthesis (FSS). However, due to the high costs of obtaining hand-drawn sketch datasets, there lacks a complete be…
EDN: Salient Object Detection via Extremely-Downsampled Network
Yu-Huan Wu, Yun Liu, Le Zhang +2
Recent progress on salient object detection (SOD) mainly benefits from multi-scale learning, where the high-level and low-level features collaborate in locating salient objects and…
A Learning-based Method for Online Adjustment of C-arm Cone-Beam CT Source Trajectories for Artifact Avoidance
Mareike Thies, Jan-Nico Zäch, Cong Gao +4
During spinal fusion surgery, screws are placed close to critical nerves suggesting the need for highly accurate screw placement. Verifying screw placement on high-quality tomograp…
Multi-Task Learning for Dense Prediction Tasks: A Survey
Simon Vandenhende, Stamatios Georgoulis, Wouter Van Gansbeke +3
With the advent of deep learning, many dense prediction tasks, i.e. tasks that produce pixel-level predictions, have seen significant performance improvements. The typical approach…
SESAME: Semantic Editing of Scenes by Adding, Manipulating or Erasing Objects
Evangelos Ntavelis, Andrés Romero, Iason Kastanis +2
Recent advances in image generation gave rise to powerful tools for semantic image editing. However, existing approaches can either operate on a single image or require an abundanc…
Geometrically Mappable Image Features
Janine Thoma, Danda Pani Paudel, Ajad Chhatkuli +1
Vision-based localization of an agent in a map is an important problem in robotics and computer vision. In that context, localization by learning matchable image features is gainin…
Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression
Yawei Li, Shuhang Gu, Christoph Mayer +2
In this paper, we analyze two popular network compression techniques, i.e. filter pruning and low-rank decomposition, in a unified sense. By simply changing the way the sparsity re…
DHP: Differentiable Meta Pruning via HyperNetworks
Yawei Li, Shuhang Gu, Kai Zhang +2
Network pruning has been the driving force for the acceleration of neural networks and the alleviation of model storage/transmission burden. With the advent of AutoML and neural ar…
Self-supervised Object Motion and Depth Estimation from Video
Qi Dai, Vaishakh Patil, Simon Hecker +3
We present a self-supervised learning framework to estimate the individual object motion and monocular depth from video. We model the object motion as a 6 degree-of-freedom rigid-b…
Semantic Understanding of Foggy Scenes with Purely Synthetic Data
Martin Hahner, Dengxin Dai, Christos Sakaridis +2
This work addresses the problem of semantic scene understanding under foggy road conditions. Although marked progress has been made in semantic scene understanding over the recent…
Dual Grid Net: hand mesh vertex regression from single depth maps
Chengde Wan, Thomas Probst, Luc Van Gool +1
We present a method for recovering the dense 3D surface of the hand by regressing the vertex coordinates of a mesh model from a single depth map. To this end, we use a two-stage 2D…
Weighted Mean Curvature
Yuanhao Gong, Orcun Goksel
In image processing tasks, spatial priors are essential for robust computations, regularization, algorithmic design and Bayesian inference. In this paper, we introduce weighted mea…
Precise Proximal Femur Fracture Classification for Interactive Training and Surgical Planning
Amelia Jiménez-Sánchez, Anees Kazi, Shadi Albarqouni +5
We demonstrate the feasibility of a fully automatic computer-aided diagnosis (CAD) tool, based on deep learning, that localizes and classifies proximal femur fractures on X-ray ima…
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
Andrey Ignatov, Radu Timofte, Thang Van Vu +45
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones. The challenge consisted of two tra…
Highly-Economized Multi-View Binary Compression for Scalable Image Clustering
Zheng Zhang, Li Liu, Jie Qin +5
How to economically cluster large-scale multi-view images is a long-standing problem in computer vision. To tackle this challenge, we introduce a novel approach named Highly-econom…
Combining Heterogeneously Labeled Datasets For Training Segmentation Networks
Jana Kemnitz, Christian F. Baumgartner, Wolfgang Wirth +3
Accurate segmentation of medical images is an important step towards analyzing and tracking disease related morphological alterations in the anatomy. Convolutional neural networks…
Deep Generative Models in the Real-World: An Open Challenge from Medical Imaging
Xiaoran Chen, Nick Pawlowski, Martin Rajchl +2
Recent advances in deep learning led to novel generative modeling techniques that achieve unprecedented quality in generated samples and performance in learning complex distributio…
Blazingly Fast Video Object Segmentation with Pixel-Wise Metric Learning
Yuhua Chen, Jordi Pont-Tuset, Alberto Montes +1
This paper tackles the problem of video object segmentation, given some user annotation which indicates the object of interest. The problem is formulated as pixel-wise retrieval in…
Learning the Synthesizability of Dynamic Texture Samples
Feng Yang, Gui-Song Xia, Dengxin Dai +1
A dynamic texture (DT) refers to a sequence of images that exhibit temporal regularities and has many applications in computer vision and graphics. Given an exemplar of dynamic tex…
MR image reconstruction using deep density priors
Kerem C. Tezcan, Christian F. Baumgartner, Roger Luechinger +2
Algorithms for Magnetic Resonance (MR) image reconstruction from undersampled measurements exploit prior information to compensate for missing k-space data. Deep learning (DL) prov…
Appearance-and-Relation Networks for Video Classification
Limin Wang, Wei Li, Wen Li +1
Spatiotemporal feature learning in videos is a fundamental problem in computer vision. This paper presents a new architecture, termed as Appearance-and-Relation Network (ARTNet), t…
Temporal Segment Networks for Action Recognition in Videos
Limin Wang, Yuanjun Xiong, Zhe Wang +4
Deep convolutional networks have achieved great success for image recognition. However, for action recognition in videos, their advantage over traditional methods is not so evident…
Magnetic-Visual Sensor Fusion based Medical SLAM for Endoscopic Capsule Robot
Mehmet Turan, Yasin Almalioglu, Hunter Gilbert +3
A reliable, real-time simultaneous localization and mapping (SLAM) method is crucial for the navigation of actively controlled capsule endoscopy robots. These robots are an emergin…