560 citations · 782 across the 15 of their papers we have counts for
15 papers
Graph Attention Transformer Network for Multi-Label Image Classification
Jin Yuan, Shikai Chen, Yao Zhang +4
Multi-label classification aims to recognize multiple objects or attributes from images. However, it is challenging to learn from proper label graphs to effectively characterize su…
A Survey of Visual Transformers
Yang Liu, Yao Zhang, Yixin Wang +7
Transformer, an attention-based encoder-decoder model, has already revolutionized the field of natural language processing (NLP). Inspired by such significant achievements, some pi…
ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing Modalities
Yixin Wang, Yang Zhang, Yang Liu +6
Accurate segmentation of brain tumors from magnetic resonance imaging (MRI) is clinically relevant in diagnoses, prognoses and surgery treatment, which requires multiple modalities…
"Do You Know You Are Tracked by Photos That You Didn't Take": Location-Aware Multi-Party Image Privacy Protection
Joshua Morris, Sara Newman, Kannappan Palaniappan +2
Most existing image privacy protection works focus mainly on the privacy of photo owners and their friends, but lack the consideration of other people who are in the background of…
Cluster-level Feature Alignment for Person Re-identification
Qiuyu Chen, Wei Zhang, Jianping Fan
Instance-level alignment is widely exploited for person re-identification, e.g. spatial alignment, latent semantic alignment and triplet alignment. This paper probes another featur…
Automatic Image Labelling at Pixel Level
Xiang Zhang, Wei Zhang, Jinye Peng +1
The performance of deep networks for semantic image segmentation largely depends on the availability of large-scale training images which are labelled at the pixel level. Typically…
Adaptive Fractional Dilated Convolution Network for Image Aesthetics Assessment
Qiuyu Chen, Wei Zhang, Ning Zhou +4
To leverage deep learning for image aesthetics assessment, one critical but unsolved issue is how to seamlessly incorporate the information of image aspect ratios to learn more rob…
MOD: A Deep Mixture Model with Online Knowledge Distillation for Large Scale Video Temporal Concept Localization
Rongcheng Lin, Jing Xiao, Jianping Fan
In this paper, we present and discuss a deep mixture model with online knowledge distillation (MOD) for large-scale video temporal concept localization, which is ranked 3rd in the…
Learning Competitive and Discriminative Reconstructions for Anomaly Detection
Kai Tian, Shuigeng Zhou, Jianping Fan +1
Most of the existing methods for anomaly detection use only positive data to learn the data distribution, thus they usually need a pre-defined threshold at the detection stage to d…
NeXtVLAD: An Efficient Neural Network to Aggregate Frame-level Features for Large-scale Video Classification
Rongcheng Lin, Jing Xiao, Jianping Fan
This paper introduces a fast and efficient network architecture, NeXtVLAD, to aggregate frame-level features into a compact feature vector for large-scale video classification. Bri…
Multi-modal Factorized Bilinear Pooling with Co-Attention Learning for Visual Question Answering
Zhou Yu, Jun Yu, Jianping Fan +1
Visual question answering (VQA) is challenging because it requires a simultaneous understanding of both the visual content of images and the textual content of questions. The appro…
Beyond Bilinear: Generalized Multimodal Factorized High-order Pooling for Visual Question Answering
Zhou Yu, Jun Yu, Chenchao Xiang +2
Visual question answering (VQA) is challenging because it requires a simultaneous understanding of both visual content of images and textual content of questions. To support the VQ…
Embedding Visual Hierarchy with Deep Networks for Large-Scale Visual Recognition
Tianyi Zhao, Baopeng Zhang, Wei Zhang +3
In this paper, a level-wise mixture model (LMM) is developed by embedding visual hierarchy with deep networks to support large-scale visual recognition (i.e., recognizing thousands…
Deep Mixture of Diverse Experts for Large-Scale Visual Recognition
Tianyi Zhao, Jun Yu, Zhenzhong Kuang +2
In this paper, a deep mixture of diverse experts algorithm is developed for seamlessly combining a set of base deep CNNs (convolutional neural networks) with diverse outputs (task…
Re-ranking Object Proposals for Object Detection in Automatic Driving
Zhun Zhong, Mingyi Lei, Shaozi Li +1
Object detection often suffers from a plenty of bootless proposals, selecting high quality proposals remains a great challenge. In this paper, we propose a semantic, class-specific…