251 citations · 977 across the 32 of their papers we have counts for
18 papers · 1 filter
EmoSet: A Large-scale Visual Emotion Dataset with Rich Attributes
Jingyuan Yang, Qirui Huang, Tingting Ding +3
Visual Emotion Analysis (VEA) aims at predicting people's emotional responses to visual stimuli. This is a promising, yet challenging, task in affective computing, which has drawn…
Adjusting Logit in Gaussian Form for Long-Tailed Visual Recognition
Mengke Li, Yiu-ming Cheung, Yang Lu +3
It is not uncommon that real-world data are distributed with a long tail. For such data, the learning of deep neural networks becomes challenging because it is hard to classify tai…
Active Self-Training for Weakly Supervised 3D Scene Semantic Segmentation
Gengxin Liu, Oliver van Kaick, Hui Huang +1
Since the preparation of labeled data for training semantic segmentation networks of point clouds is a time-consuming process, weakly supervised approaches have been introduced to…
Optimized Views Photogrammetry: Precision Analysis and A Large-scale Case Study in Qingdao
Qingquan Li, Wenshuai Yu, San Jiang
UAVs have become one of the widely used remote sensing platforms and played a critical role in the construction of smart cities. However, due to the complex environment in urban sc…
FEAT: Face Editing with Attention
Xianxu Hou, Linlin Shen, Or Patashnik +2
Employing the latent space of pretrained generators has recently been shown to be an effective means for GAN-based face manipulation. The success of this approach heavily relies on…
Point cloud completion via structured feature maps using a feedback network
Zejia Su, Haibin Huang, Chongyang Ma +2
In this paper, we tackle the challenging problem of point cloud completion from the perspective of feature learning. Our key observation is that to recover the underlying structure…
ShapeFormer: Transformer-based Shape Completion via Sparse Representation
Xingguang Yan, Liqiang Lin, Niloy J. Mitra +3
We present ShapeFormer, a transformer-based network that produces a distribution of object completions, conditioned on incomplete, and possibly noisy, point clouds. The resultant d…
Capturing, Reconstructing, and Simulating: the UrbanScene3D Dataset
Liqiang Lin, Yilin Liu, Yue Hu +3
We present UrbanScene3D, a large-scale data platform for research of urban scene perception and reconstruction. UrbanScene3D contains over 128k high-resolution images covering 16 s…
On Learning the Right Attention Point for Feature Enhancement
Liqiang Lin, Pengdi Huang, Chi-Wing Fu +3
We present a novel attention-based mechanism to learn enhanced point features for point cloud processing tasks, e.g., classification and segmentation. Unlike prior works, which wer…
Object Properties Inferring from and Transfer for Human Interaction Motions
Qian Zheng, Weikai Wu, Hanting Pan +3
Humans regularly interact with their surrounding objects. Such interactions often result in strongly correlated motion between humans and the interacting objects. We thus ask: "Is…
Abnormal activity capture from passenger flow of elevator based on unsupervised learning and fine-grained multi-label recognition
Chunhua Jia, Wenhai Yi, Yu Wu +3
We present a work-flow which aims at capturing residents' abnormal activities through the passenger flow of elevator in multi-storey residence buildings. Camera and sensors (hall s…
RPM-Net: Recurrent Prediction of Motion and Parts from Point Cloud
Zihao Yan, Ruizhen Hu, Xingguang Yan +4
We introduce RPM-Net, a deep learning-based approach which simultaneously infers movable parts and hallucinates their motions from a single, un-segmented, and possibly partial, 3D…
Graph2Plan: Learning Floorplan Generation from Layout Graphs
Ruizhen Hu, Zeyu Huang, Yuhan Tang +3
We introduce a learning framework for automated floorplan generation which combines generative modeling using deep neural networks and user-in-the-loop designs to enable human user…
GraftNet: An Engineering Implementation of CNN for Fine-grained Multi-label Task
Chunhua Jia, Lei Zhang, Hui Huang +3
Multi-label networks with branches are proved to perform well in both accuracy and speed, but lacks flexibility in providing dynamic extension onto new labels due to the low effici…
LOGAN: Unpaired Shape Transform in Latent Overcomplete Space
Kangxue Yin, Zhiqin Chen, Hui Huang +2
We introduce LOGAN, a deep neural network aimed at learning general-purpose shape transforms from unpaired domains. The network is trained on two sets of shapes, e.g., tables and c…
Patch-based Progressive 3D Point Set Upsampling
Wang Yifan, Shihao Wu, Hui Huang +2
We present a detail-driven deep neural network for point set upsampling. A high-resolution point set is essential for point-based rendering and surface reconstruction. Inspired by…
EFANet: Exchangeable Feature Alignment Network for Arbitrary Style Transfer
Zhijie Wu, Chunjin Song, Yang Zhou +2
Style transfer has been an important topic both in computer vision and graphics. Since the seminal work of Gatys et al. first demonstrates the power of stylization through optimiza…
Specular-to-Diffuse Translation for Multi-View Reconstruction
Shihao Wu, Hui Huang, Tiziano Portenier +4
Most multi-view 3D reconstruction algorithms, especially when shape-from-shading cues are used, assume that object appearance is predominantly diffuse. To alleviate this restrictio…