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20092022
most citedWeakly Aligned Feature Fusion for Multimodal Object Detection

97 citations · 189 across the 18 of their papers we have counts for

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13 papers · 1 filter

cs.CV2022★ 1 cited

Automatically Discovering Novel Visual Categories with Self-supervised Prototype Learning

Lu Zhang, Lu Qi, Xu Yang +3

This paper tackles the problem of novel category discovery (NCD), which aims to discriminate unknown categories in large-scale image collections. The NCD task is challenging due to…

cs.CV2022

Zero-shot object goal visual navigation

Qianfan Zhao, Lu Zhang, Bin He +2

Object goal visual navigation is a challenging task that aims to guide a robot to find the target object based on its visual observation, and the target is limited to the classes p…

cs.CV2022★ 97 cited

Weakly Aligned Feature Fusion for Multimodal Object Detection

Lu Zhang, Zhiyong Liu, Xiangyu Zhu +4

To achieve accurate and robust object detection in the real-world scenario, various forms of images are incorporated, such as color, thermal, and depth. However, multimodal data of…

cs.CV2022

Unseen Object Instance Segmentation with Fully Test-time RGB-D Embeddings Adaptation

Lu Zhang, Siqi Zhang, Xu Yang +2

Segmenting unseen objects is a crucial ability for the robot since it may encounter new environments during the operation. Recently, a popular solution is leveraging RGB-D features…

cs.CV2018★ 1 cited

Multi-view Laplacian Eigenmaps Based on Bag-of-Neighbors For RGBD Human Emotion Recognition

Shenglan Liu, Shuai Guo, Hong Qiao +6

Human emotion recognition is an important direction in the field of biometric and information forensics. However, most existing human emotion research are based on the single RGB v…

cs.CV2017★ 16 cited

Discriminatively Boosted Image Clustering with Fully Convolutional Auto-Encoders

Fengfu Li, Hong Qiao, Bo Zhang +1

Traditional image clustering methods take a two-step approach, feature learning and clustering, sequentially. However, recent research results demonstrated that combining the separ…

cs.CV2017★ 6 cited

A Fast and Compact Saliency Score Regression Network Based on Fully Convolutional Network

Xuanyang Xi, Yongkang Luo, Fengfu Li +2

Visual saliency detection aims at identifying the most visually distinctive parts in an image, and serves as a pre-processing step for a variety of computer vision and image proces…

cs.CV2016

Poisson Noise Reduction with Higher-order Natural Image Prior Model

Wensen Feng, Hong Qiao, Yunjin Chen

Poisson denoising is an essential issue for various imaging applications, such as night vision, medical imaging and microscopy. State-of-the-art approaches are clearly dominated by…

cs.CV2016

A New Manifold Distance Measure for Visual Object Categorization

Fengfu Li, Xiayuan Huang, Hong Qiao +1

Manifold distances are very effective tools for visual object recognition. However, most of the traditional manifold distances between images are based on the pixel-level compariso…

cs.CV2014★ 34 cited

A higher-order MRF based variational model for multiplicative noise reduction

Yunjin Chen, Wensen Feng, René Ranftl +2

The Fields of Experts (FoE) image prior model, a filter-based higher-order Markov Random Fields (MRF) model, has been shown to be effective for many image restoration problems. Mot…

cs.CV2013★ 1 cited

GNCGCP - Graduated NonConvexity and Graduated Concavity Procedure

Zhi-Yong Liu, Hong Qiao

In this paper we propose the Graduated NonConvexity and Graduated Concavity Procedure (GNCGCP) as a general optimization framework to approximately solve the combinatorial optimiza…

cs.CV2010★ 23 cited

Intrinsic dimension estimation of data by principal component analysis

Mingyu Fan, Nannan Gu, Hong Qiao +1

Estimating intrinsic dimensionality of data is a classic problem in pattern recognition and statistics. Principal Component Analysis (PCA) is a powerful tool in discovering dimensi…

cs.CV2010

An Explicit Nonlinear Mapping for Manifold Learning

Hong Qiao, Peng Zhang, Di Wang +1

Manifold learning is a hot research topic in the field of computer science and has many applications in the real world. A main drawback of manifold learning methods is, however, th…