57 citations · 133 across the 21 of their papers we have counts for
21 papers
Recursive Counterfactual Deconfounding for Object Recognition
Jiayin Sun, Hong Wang, Qiulei Dong
Image recognition is a classic and common task in the computer vision field, which has been widely applied in the past decade. Most existing methods in literature aim to learn disc…
Two-in-One Depth: Bridging the Gap Between Monocular and Binocular Self-supervised Depth Estimation
Zhengming Zhou, Qiulei Dong
Monocular and binocular self-supervised depth estimations are two important and related tasks in computer vision, which aim to predict scene depths from single images and stereo im…
Complementary Frequency-Varying Awareness Network for Open-Set Fine-Grained Image Recognition
Qiulei Dong, Jiayin Sun, Mengyu Gao
Open-set image recognition is a challenging topic in computer vision. Most of the existing works in literature focus on learning more discriminative features from the input images,…
Spatial-Temporal Attention Network for Open-Set Fine-Grained Image Recognition
Jiayin Sun, Hong Wang, Qiulei Dong
Triggered by the success of transformers in various visual tasks, the spatial self-attention mechanism has recently attracted more and more attention in the computer vision communi…
Self-distilled Feature Aggregation for Self-supervised Monocular Depth Estimation
Zhengming Zhou, Qiulei Dong
Self-supervised monocular depth estimation has received much attention recently in computer vision. Most of the existing works in literature aggregate multi-scale features for dept…
Descriptor Distillation: a Teacher-Student-Regularized Framework for Learning Local Descriptors
Yuzhen Liu, Qiulei Dong
Learning a fast and discriminative patch descriptor is a challenging topic in computer vision. Recently, many existing works focus on training various descriptor learning networks…
Orthogonal-Coding-Based Feature Generation for Transductive Open-Set Recognition via Dual-Space Consistent Sampling
Jiayin Sun, Qiulei Dong
Open-set recognition (OSR) aims to simultaneously detect unknown-class samples and classify known-class samples. Most of the existing OSR methods are inductive methods, which gener…
An Iterative Co-Training Transductive Framework for Zero Shot Learning
Bo Liu, Lihua Hu, Qiulei Dong +1
In zero-shot learning (ZSL) community, it is generally recognized that transductive learning performs better than inductive one as the unseen-class samples are also used in its tra…
Semantic-diversity transfer network for generalized zero-shot learning via inner disagreement based OOD detector
Bo Liu, Qiulei Dong, Zhanyi Hu
Zero-shot learning (ZSL) aims to recognize objects from unseen classes, where the kernel problem is to transfer knowledge from seen classes to unseen classes by establishing approp…
Learning Occlusion-Aware Coarse-to-Fine Depth Map for Self-supervised Monocular Depth Estimation
Zhengming Zhou, Qiulei Dong
Self-supervised monocular depth estimation, aiming to learn scene depths from single images in a self-supervised manner, has received much attention recently. In spite of recent ef…
Pursuing 3D Scene Structures with Optical Satellite Images from Affine Reconstruction to Euclidean Reconstruction
Pinhe Wang, Limin Shi, Bao Chen +3
How to use multiple optical satellite images to recover the 3D scene structure is a challenging and important problem in the remote sensing field. Most existing methods in literatu…
HardBoost: Boosting Zero-Shot Learning with Hard Classes
Bo Liu, Lihua Hu, Zhanyi Hu +1
This work is a systematical analysis on the so-called hard class problem in zero-shot learning (ZSL), that is, some unseen classes disproportionally affect the ZSL performances tha…
Rotation Transformation Network: Learning View-Invariant Point Cloud for Classification and Segmentation
Shuang Deng, Bo Liu, Qiulei Dong +1
Many recent works show that a spatial manipulation module could boost the performances of deep neural networks (DNNs) for 3D point cloud analysis. In this paper, we aim to provide…
GA-NET: Global Attention Network for Point Cloud Semantic Segmentation
Shuang Deng, Qiulei Dong
How to learn long-range dependencies from 3D point clouds is a challenging problem in 3D point cloud analysis. Addressing this problem, we propose a global attention network for po…
Superpoint-guided Semi-supervised Semantic Segmentation of 3D Point Clouds
Shuang Deng, Qiulei Dong, Bo Liu +1
3D point cloud semantic segmentation is a challenging topic in the computer vision field. Most of the existing methods in literature require a large amount of fully labeled trainin…
Language-Level Semantics Conditioned 3D Point Cloud Segmentation
Bo Liu, Shuang Deng, Qiulei Dong +1
In this work, a language-level Semantics Conditioned framework for 3D Point cloud segmentation, called SeCondPoint, is proposed, where language-level semantics are introduced to co…
Hardness Sampling for Self-Training Based Transductive Zero-Shot Learning
Liu Bo, Qiulei Dong, Zhanyi Hu
Transductive zero-shot learning (T-ZSL) which could alleviate the domain shift problem in existing ZSL works, has received much attention recently. However, an open problem in T-ZS…
Zero-Shot Learning from Adversarial Feature Residual to Compact Visual Feature
Bo Liu, Qiulei Dong, Zhanyi Hu
Recently, many zero-shot learning (ZSL) methods focused on learning discriminative object features in an embedding feature space, however, the distributions of the unseen-class fea…
Face representation by deep learning: a linear encoding in a parameter space?
Qiulei Dong, Jiayin Sun, Zhanyi Hu
Recently, Convolutional Neural Networks (CNNs) have achieved tremendous performances on face recognition, and one popular perspective regarding CNNs' success is that CNNs could lea…
Non-uniqueness phenomenon of object representation in modelling IT cortex by deep convolutional neural network (DCNN)
Qiulei Dong, Bo Liu, Zhanyi Hu
Recently DCNN (Deep Convolutional Neural Network) has been advocated as a general and promising modelling approach for neural object representation in primate inferotemporal cortex…
Statistics of Visual Responses to Object Stimuli from Primate AIT Neurons to DNN Neurons
Qiulei Dong, Zhanyi Hu
Cadieu et al. (Cadieu,2014) reported that deep neural networks(DNNs) could rival the representation of primate inferotemporal cortex for object recognition. Lehky et al. (Lehky,201…