329 citations · 386 across the 19 of their papers we have counts for
17 papers · 1 filter
Flattening-Net: Deep Regular 2D Representation for 3D Point Cloud Analysis
Qijian Zhang, Junhui Hou, Yue Qian +3
Point clouds are characterized by irregularity and unstructuredness, which pose challenges in efficient data exploitation and discriminative feature extraction. In this paper, we p…
PointMCD: Boosting Deep Point Cloud Encoders via Multi-view Cross-modal Distillation for 3D Shape Recognition
Qijian Zhang, Junhui Hou, Yue Qian
As two fundamental representation modalities of 3D objects, 3D point clouds and multi-view 2D images record shape information from different domains of geometric structures and vis…
WarpingGAN: Warping Multiple Uniform Priors for Adversarial 3D Point Cloud Generation
Yingzhi Tang, Yue Qian, Qijian Zhang +3
We propose WarpingGAN, an effective and efficient 3D point cloud generation network. Unlike existing methods that generate point clouds by directly learning the mapping functions b…
IDEA-Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding Alignment
Yiming Zeng, Yue Qian, Qijian Zhang +3
This paper investigates the problem of temporally interpolating dynamic 3D point clouds with large non-rigid deformation. We formulate the problem as estimation of point-wise traje…
ParaNet: Deep Regular Representation for 3D Point Clouds
Qijian Zhang, Junhui Hou, Yue Qian +2
Although convolutional neural networks have achieved remarkable success in analyzing 2D images/videos, it is still non-trivial to apply the well-developed 2D techniques in regular…
CorrNet3D: Unsupervised End-to-end Learning of Dense Correspondence for 3D Point Clouds
Yiming Zeng, Yue Qian, Zhiyu Zhu +3
Motivated by the intuition that one can transform two aligned point clouds to each other more easily and meaningfully than a misaligned pair, we propose CorrNet3D -- the first unsu…
Deep Magnification-Flexible Upsampling over 3D Point Clouds
Yue Qian, Junhui Hou, Sam Kwong +1
This paper addresses the problem of generating dense point clouds from given sparse point clouds to model the underlying geometric structures of objects/scenes. To tackle this chal…
MOPS-Net: A Matrix Optimization-driven Network forTask-Oriented 3D Point Cloud Downsampling
Yue Qian, Junhui Hou, Qijian Zhang +3
This paper explores the problem of task-oriented downsampling over 3D point clouds, which aims to downsample a point cloud while maintaining the performance of subsequent applicati…
PUGeo-Net: A Geometry-centric Network for 3D Point Cloud Upsampling
Yue Qian, Junhui Hou, Sam Kwong +1
This paper addresses the problem of generating uniform dense point clouds to describe the underlying geometric structures from given sparse point clouds. Due to the irregular and u…
Nested Network with Two-Stream Pyramid for Salient Object Detection in Optical Remote Sensing Images
Chongyi Li, Runmin Cong, Junhui Hou +3
Arising from the various object types and scales, diverse imaging orientations, and cluttered backgrounds in optical remote sensing image (RSI), it is difficult to directly extend…
Instance Retrieval at Fine-grained Level Using Multi-Attribute Recognition
Roshanak Zakizadeh, Yu Qian, Michele Sasdelli +1
In this paper, we present a method for instance ranking and retrieval at fine-grained level based on the global features extracted from a multi-attribute recognition model which is…
Improving the Annotation of DeepFashion Images for Fine-grained Attribute Recognition
Roshanak Zakizadeh, Michele Sasdelli, Yu Qian +1
DeepFashion is a widely used clothing dataset with 50 categories and more than overall 200k images where each image is annotated with fine-grained attributes. This dataset is often…
FineTag: Multi-attribute Classification at Fine-grained Level in Images
Roshanak Zakizadeh, Michele Sasdelli, Yu Qian +1
In this paper, we address the extraction of the fine-grained attributes of an instance as a `multi-attribute classification' problem. To this end, we propose an end-to-end architec…
Pillar Networks++: Distributed non-parametric deep and wide networks
Biswa Sengupta, Yu Qian
In recent work, it was shown that combining multi-kernel based support vector machines (SVMs) can lead to near state-of-the-art performance on an action recognition dataset (HMDB-5…
Multi-kernel learning of deep convolutional features for action recognition
Biswa Sengupta, Yu Qian
Image understanding using deep convolutional network has reached human-level performance, yet a closely related problem of video understanding especially, action recognition has no…
Algorithmic clothing: hybrid recommendation, from street-style-to-shop
Y Qian, P Giaccone, M Sasdelli +2
In this paper we detail Cortexica's (https://www.cortexica.com) recommendation framework -- particularly, we describe how a hybrid visual recommender system can be created by combi…
Large-scale image analysis using docker sandboxing
B Sengupta, E Vazquez, M Sasdelli +4
With the advent of specialized hardware such as Graphics Processing Units (GPUs), large scale image localization, classification and retrieval have seen increased prevalence. Desig…