Search Results for author: Xiangbo Shu

Found 17 papers, 2 papers with code

PNP: Robust Learning From Noisy Labels by Probabilistic Noise Prediction

no code implementations CVPR 2022 Zeren Sun, Fumin Shen, Dan Huang, Qiong Wang, Xiangbo Shu, Yazhou Yao, Jinhui Tang

Label noise has been a practical challenge in deep learning due to the strong capability of deep neural networks in fitting all training data.

Expansion-Squeeze-Excitation Fusion Network for Elderly Activity Recognition

no code implementations21 Dec 2021 Xiangbo Shu, Jiawen Yang, Rui Yan, Yan Song

This work focuses on the task of elderly activity recognition, which is a challenging task due to the existence of individual actions and human-object interactions in elderly activities.

Action Recognition Human-Object Interaction Detection

Interactive Fusion of Multi-level Features for Compositional Activity Recognition

1 code implementation10 Dec 2020 Rui Yan, Lingxi Xie, Xiangbo Shu, Jinhui Tang

To understand a complex action, multiple sources of information, including appearance, positional, and semantic features, need to be integrated.

Action Recognition

Data-driven Meta-set Based Fine-Grained Visual Classification

1 code implementation6 Aug 2020 Chuanyi Zhang, Yazhou Yao, Xiangbo Shu, Zechao Li, Zhenmin Tang, Qi Wu

To this end, we propose a data-driven meta-set based approach to deal with noisy web images for fine-grained recognition.

Classification Fine-Grained Image Classification +3

Social Adaptive Module for Weakly-supervised Group Activity Recognition

no code implementations ECCV 2020 Rui Yan, Lingxi Xie, Jinhui Tang, Xiangbo Shu, Qi Tian

This paper presents a new task named weakly-supervised group activity recognition (GAR) which differs from conventional GAR tasks in that only video-level labels are available, yet the important persons within each frame are not provided even in the training data.

Group Activity Recognition

Spatiotemporal Co-attention Recurrent Neural Networks for Human-Skeleton Motion Prediction

no code implementations29 Sep 2019 Xiangbo Shu, Liyan Zhang, Guo-Jun Qi, Wei Liu, Jinhui Tang

To this end, we propose a novel Skeleton-joint Co-attention Recurrent Neural Networks (SC-RNN) to capture the spatial coherence among joints, and the temporal evolution among skeletons simultaneously on a skeleton-joint co-attention feature map in spatiotemporal space.

Human motion prediction motion prediction

Region-Manipulated Fusion Networks for Pancreatitis Recognition

no code implementations3 Jul 2019 Jian Wang, Xiaoyao Li, Xiangbo Shu, Weiqin Li

Specifically, to effectively highlight the imperceptible lesion regions, a novel region-manipulated scheme in RMFN is proposed to force the lesion regions while weaken the non-lesion regions by ceaselessly aggregating the multi-scale local information onto feature maps.

Object Recognition

Hierarchical Long Short-Term Concurrent Memory for Human Interaction Recognition

no code implementations1 Nov 2018 Xiangbo Shu, Jinhui Tang, Guo-Jun Qi, Wei Liu, Jian Yang

In a Co-LSTM unit, each sub-memory unit stores individual motion information, while this Co-LSTM unit selectively integrates and stores inter-related motion information between multiple interacting persons from multiple sub-memory units via the cell gate and co-memory cell, respectively.

Action Recognition Human Interaction Recognition

Deep Ordinal Hashing with Spatial Attention

no code implementations7 May 2018 Lu Jin, Xiangbo Shu, Kai Li, Zechao Li, Guo-Jun Qi, Jinhui Tang

However, most existing deep hashing methods directly learn the hash functions by encoding the global semantic information, while ignoring the local spatial information of images.

Image Retrieval

Social Anchor-Unit Graph Regularized Tensor Completion for Large-Scale Image Retagging

no code implementations12 Apr 2018 Jinhui Tang, Xiangbo Shu, Zechao Li, Yu-Gang Jiang, Qi Tian

Recent approaches simultaneously explore visual, user and tag information to improve the performance of image retagging by constructing and exploring an image-tag-user graph.

Graph Learning TAG

Face Aging with Contextual Generative Adversarial Nets

no code implementations1 Feb 2018 Si Liu, Yao Sun, Defa Zhu, Renda Bao, Wei Wang, Xiangbo Shu, Shuicheng Yan

The age discriminative network guides the synthesized face to fit the real conditional distribution.

Face Verification

Personalized Age Progression with Bi-level Aging Dictionary Learning

no code implementations4 Jun 2017 Xiangbo Shu, Jinhui Tang, Zechao Li, Hanjiang Lai, Liyan Zhang, Shuicheng Yan

Basically, for each age group, we learn an aging dictionary to reveal its aging characteristics (e. g., wrinkles), where the dictionary bases corresponding to the same index yet from two neighboring aging dictionaries form a particular aging pattern cross these two age groups, and a linear combination of all these patterns expresses a particular personalized aging process.

Dictionary Learning Face Verification

Concurrence-Aware Long Short-Term Sub-Memories for Person-Person Action Recognition

no code implementations3 Jun 2017 Xiangbo Shu, Jinhui Tang, Guo-Jun Qi, Yan Song, Zechao Li, Liyan Zhang

To this end, we propose a novel Concurrence-Aware Long Short-Term Sub-Memories (Co-LSTSM) to model the long-term inter-related dynamics between two interacting people on the bounding boxes covering people.

Action Recognition

Recurrent Face Aging

no code implementations CVPR 2016 Wei Wang, Zhen Cui, Yan Yan, Jiashi Feng, Shuicheng Yan, Xiangbo Shu, Nicu Sebe

Modeling the aging process of human face is important for cross-age face verification and recognition.

Face Verification

Instance-Aware Hashing for Multi-Label Image Retrieval

no code implementations10 Mar 2016 Hanjiang Lai, Pan Yan, Xiangbo Shu, Yunchao Wei, Shuicheng Yan

The instance-aware representations not only bring advantages to semantic hashing, but also can be used in category-aware hashing, in which an image is represented by multiple pieces of hash codes and each piece of code corresponds to a category.

Multi-Label Image Retrieval

Personalized Age Progression with Aging Dictionary

no code implementations ICCV 2015 Xiangbo Shu, Jinhui Tang, Hanjiang Lai, Luoqi Liu, Shuicheng Yan

Second, it is challenging or even impossible to collect faces of all age groups for a particular subject, yet much easier and more practical to get face pairs from neighboring age groups.

Dictionary Learning Face Verification

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