175 citations · 1.2k across the 65 of their papers we have counts for
24 papers · 1 filter
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…
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…
Detecting Deepfake by Creating Spatio-Temporal Regularity Disruption
Jiazhi Guan, Hang Zhou, Mingming Gong +3
Despite encouraging progress in deepfake detection, generalization to unseen forgery types remains a significant challenge due to the limited forgery clues explored during training…
Style Interleaved Learning for Generalizable Person Re-identification
Wentao Tan, Changxing Ding, Pengfei Wang +2
Domain generalization (DG) for person re-identification (ReID) is a challenging problem, as access to target domain data is not permitted during the training process. Most existing…
Adversarial Consistency for Single Domain Generalization in Medical Image Segmentation
Yanwu Xu, Shaoan Xie, Maxwell Reynolds +3
An organ segmentation method that can generalize to unseen contrasts and scanner settings can significantly reduce the need for retraining of deep learning models. Domain Generaliz…
Few-Shot Font Generation by Learning Fine-Grained Local Styles
Licheng Tang, Yiyang Cai, Jiaming Liu +7
Few-shot font generation (FFG), which aims to generate a new font with a few examples, is gaining increasing attention due to the significant reduction in labor cost. A typical FFG…
Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation
Yanwu Xu, Shaoan Xie, Wenhao Wu +3
Unpaired image-to-image translation (I2I) is an ill-posed problem, as an infinite number of translation functions can map the source domain distribution to the target distribution.…
Unaligned Image-to-Image Translation by Learning to Reweight
Shaoan Xie, Mingming Gong, Yanwu Xu +1
Unsupervised image-to-image translation aims at learning the mapping from the source to target domain without using paired images for training. An essential yet restrictive assumpt…
Box-Adapt: Domain-Adaptive Medical Image Segmentation using Bounding BoxSupervision
Yanwu Xu, Mingming Gong, Shaoan Xie +1
Deep learning has achieved remarkable success in medicalimage segmentation, but it usually requires a large numberof images labeled with fine-grained segmentation masks, andthe ann…
Uncertainty-aware Clustering for Unsupervised Domain Adaptive Object Re-identification
Pengfei Wang, Changxing Ding, Wentao Tan +3
Unsupervised Domain Adaptive (UDA) object re-identification (Re-ID) aims at adapting a model trained on a labeled source domain to an unlabeled target domain. State-of-the-art obje…
Exploring Set Similarity for Dense Self-supervised Representation Learning
Zhaoqing Wang, Qiang Li, Guoxin Zhang +5
By considering the spatial correspondence, dense self-supervised representation learning has achieved superior performance on various dense prediction tasks. However, the pixel-lev…
Hard Example Generation by Texture Synthesis for Cross-domain Shape Similarity Learning
Huan Fu, Shunming Li, Rongfei Jia +3
Image-based 3D shape retrieval (IBSR) aims to find the corresponding 3D shape of a given 2D image from a large 3D shape database. The common routine is to map 2D images and 3D shap…
3D-FUTURE: 3D Furniture shape with TextURE
Huan Fu, Rongfei Jia, Lin Gao +4
The 3D CAD shapes in current 3D benchmarks are mostly collected from online model repositories. Thus, they typically have insufficient geometric details and less informative textur…
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…
Learning Multi-level Weight-centric Features for Few-shot Learning
Mingjiang Liang, Shaoli Huang, Shirui Pan +2
Few-shot learning is currently enjoying a considerable resurgence of interest, aided by the recent advance of deep learning. Contemporary approaches based on weight-generation sche…
Learning Depth from Monocular Videos Using Synthetic Data: A Temporally-Consistent Domain Adaptation Approach
Yipeng Mou, Mingming Gong, Huan Fu +3
Majority of state-of-the-art monocular depth estimation methods are supervised learning approaches. The success of such approaches heavily depends on the high-quality depth labels…
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…
Robust Angular Local Descriptor Learning
Yanwu Xu, Mingming Gong, Tongliang Liu +2
In recent years, the learned local descriptors have outperformed handcrafted ones by a large margin, due to the powerful deep convolutional neural network architectures such as L2-…
Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping
Huan Fu, Mingming Gong, Chaohui Wang +3
Unsupervised domain mapping aims to learn a function to translate domain X to Y by a function GXY in the absence of paired examples. Finding the optimal GXY without paired data is…
Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector
Sumedha Singla, Mingming Gong, Siamak Ravanbakhsh +3
We propose an attention-based method that aggregates local image features to a subject-level representation for predicting disease severity. In contrast to classical deep learning…
MoE-SPNet: A Mixture-of-Experts Scene Parsing Network
Huan Fu, Mingming Gong, Chaohui Wang +1
Scene parsing is an indispensable component in understanding the semantics within a scene. Traditional methods rely on handcrafted local features and probabilistic graphical models…
Deep Ordinal Regression Network for Monocular Depth Estimation
Huan Fu, Mingming Gong, Chaohui Wang +2
Monocular depth estimation, which plays a crucial role in understanding 3D scene geometry, is an ill-posed problem. Recent methods have gained significant improvement by exploring…
A Compromise Principle in Deep Monocular Depth Estimation
Huan Fu, Mingming Gong, Chaohui Wang +1
Monocular depth estimation, which plays a key role in understanding 3D scene geometry, is fundamentally an ill-posed problem. Existing methods based on deep convolutional neural ne…