175 citations · 280 across the 22 of their papers we have counts for
23 papers
Hierarchical Point-based Active Learning for Semi-supervised Point Cloud Semantic Segmentation
Zongyi Xu, Bo Yuan, Shanshan Zhao +2
Impressive performance on point cloud semantic segmentation has been achieved by fully-supervised methods with large amounts of labelled data. As it is labour-intensive to acquire…
Cross-modal & Cross-domain Learning for Unsupervised LiDAR Semantic Segmentation
Yiyang Chen, Shanshan Zhao, Changxing Ding +3
In recent years, cross-modal domain adaptation has been studied on the paired 2D image and 3D LiDAR data to ease the labeling costs for 3D LiDAR semantic segmentation (3DLSS) in th…
PNT-Edge: Towards Robust Edge Detection with Noisy Labels by Learning Pixel-level Noise Transitions
Wenjie Xuan, Shanshan Zhao, Yu Yao +5
Relying on large-scale training data with pixel-level labels, previous edge detection methods have achieved high performance. However, it is hard to manually label edges accurately…
Caption Anything: Interactive Image Description with Diverse Multimodal Controls
Teng Wang, Jinrui Zhang, Junjie Fei +5
Controllable image captioning is an emerging multimodal topic that aims to describe the image with natural language following human purpose, , looking at the specifi…
SimDistill: Simulated Multi-modal Distillation for BEV 3D Object Detection
Haimei Zhao, Qiming Zhang, Shanshan Zhao +3
Multi-view camera-based 3D object detection has become popular due to its low cost, but accurately inferring 3D geometry solely from camera data remains challenging and may lead to…
OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System
Chao Xue, Wei Liu, Shuai Xie +25
Automated machine learning (AutoML) seeks to build ML models with minimal human effort. While considerable research has been conducted in the area of AutoML in general, aiming to t…
Adaptive Edge-to-Edge Interaction Learning for Point Cloud Analysis
Shanshan Zhao, Mingming Gong, Xi Li +1
Recent years have witnessed the great success of deep learning on various point cloud analysis tasks, e.g., classification and semantic segmentation. Since point cloud data is spar…
DeepSolo: Let Transformer Decoder with Explicit Points Solo for Text Spotting
Maoyuan Ye, Jing Zhang, Shanshan Zhao +4
End-to-end text spotting aims to integrate scene text detection and recognition into a unified framework. Dealing with the relationship between the two sub-tasks plays a pivotal ro…
MetaComp: Learning to Adapt for Online Depth Completion
Yang Chen, Shanshan Zhao, Wei Ji +2
Relying on deep supervised or self-supervised learning, previous methods for depth completion from paired single image and sparse depth data have achieved impressive performance in…
MeshMAE: Masked Autoencoders for 3D Mesh Data Analysis
Yaqian Liang, Shanshan Zhao, Baosheng Yu +2
Recently, self-supervised pre-training has advanced Vision Transformers on various tasks w.r.t. different data modalities, e.g., image and 3D point cloud data. In this paper, we ex…
DPText-DETR: Towards Better Scene Text Detection with Dynamic Points in Transformer
Maoyuan Ye, Jing Zhang, Shanshan Zhao +3
Recently, Transformer-based methods, which predict polygon points or Bezier curve control points for localizing texts, are popular in scene text detection. However, these methods b…
Iterative Geometry-Aware Cross Guidance Network for Stereo Image Inpainting
Ang Li, Shanshan Zhao, Qingjie Zhang +1
Currently, single image inpainting has achieved promising results based on deep convolutional neural networks. However, inpainting on stereo images with missing regions has not bee…
FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysis
Yu Feng, Benteng Ma, Jing Zhang +3
In recent years, the security of AI systems has drawn increasing research attention, especially in the medical imaging realm. To develop a secure medical image analysis (MIA) syste…
AIM 2020: Scene Relighting and Illumination Estimation Challenge
Majed El Helou, Ruofan Zhou, Sabine Süsstrunk +34
We review the AIM 2020 challenge on virtual image relighting and illumination estimation. This paper presents the novel VIDIT dataset used in the challenge and the different propos…
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
Pengxu Wei, Hannan Lu, Radu Timofte +68
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020. This ch…
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
Kai Zhang, Martin Danelljan, Yawei Li +75
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an in…
AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results
Dario Fuoli, Zhiwu Huang, Shuhang Gu +23
This paper reviews the video extreme super-resolution challenge associated with the AIM 2020 workshop at ECCV 2020. Common scaling factors for learned video super-resolution (VSR)…
Short-Term and Long-Term Context Aggregation Network for Video Inpainting
Ang Li, Shanshan Zhao, Xingjun Ma +5
Video inpainting aims to restore missing regions of a video and has many applications such as video editing and object removal. However, existing methods either suffer from inaccur…
Adaptive Context-Aware Multi-Modal Network for Depth Completion
Shanshan Zhao, Mingming Gong, Huan Fu +1
Depth completion aims to recover a dense depth map from the sparse depth data and the corresponding single RGB image. The observed pixels provide the significant guidance for the r…
UDC 2020 Challenge on Image Restoration of Under-Display Camera: Methods and Results
Yuqian Zhou, Michael Kwan, Kyle Tolentino +42
This paper is the report of the first Under-Display Camera (UDC) image restoration challenge in conjunction with the RLQ workshop at ECCV 2020. The challenge is based on a newly-co…
Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation
Shanshan Zhao, Huan Fu, Mingming Gong +1
Supervised depth estimation has achieved high accuracy due to the advanced deep network architectures. Since the groundtruth depth labels are hard to obtain, recent methods try to…
Group-wise Deep Co-saliency Detection
Lina Wei, Shanshan Zhao, Omar El Farouk Bourahla +2
In this paper, we propose an end-to-end group-wise deep co-saliency detection approach to address the co-salient object discovery problem based on the fully convolutional network (…
Deep Optical Flow Estimation Via Multi-Scale Correspondence Structure Learning
Shanshan Zhao, Xi Li, Omar El Farouk Bourahla
As an important and challenging problem in computer vision, learning based optical flow estimation aims to discover the intrinsic correspondence structure between two adjacent vide…