128 citations · 489 across the 27 of their papers we have counts for
28 papers
ConflictGuide: AutoResearch Improves When Competing Behaviors Are Made Visible
Binqian Xu, Qiran Zou, Xiangbo Shu +1
When designing machine learning models, desirable properties are often in tension: improving one behavior can impair another, so task progress can depend on alleviating the conflic…
Spatiotemporal-Untrammelled Mixture of Experts for Multi-Person Motion Prediction
Zheng Yin, Chengjian Li, Xiangbo Shu +3
Comprehensively and flexibly capturing the complex spatio-temporal dependencies of human motion is critical for multi-person motion prediction. Existing methods grapple with two pr…
ASTRA: Let Arbitrary Subjects Transform in Video Editing
Fei Shen, Weihao Xu, Rui Yan +4
While existing video editing methods excel with single subjects, they struggle in dense, multi-subject scenes, frequently suffering from attention dilution and mask boundary entang…
FTMoMamba: Motion Generation with Frequency and Text State Space Models
Chengjian Li, Xiangbo Shu, Qiongjie Cui +2
Diffusion models achieve impressive performance in human motion generation. However, current approaches typically ignore the significance of frequency-domain information in capturi…
EventCrab: Harnessing Frame and Point Synergy for Event-based Action Recognition and Beyond
Meiqi Cao, Xiangbo Shu, Jiachao Zhang +3
Event-based Action Recognition (EAR) possesses the advantages of high-temporal resolution capturing and privacy preservation compared with traditional action recognition. Current l…
FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data
Binqian Xu, Xiangbo Shu, Haiyang Mei +3
Multimodal Large Language Models (MLLMs) have made significant advancements, demonstrating powerful capabilities in processing and understanding multimodal data. Fine-tuning MLLMs…
Locality-aware Cross-modal Correspondence Learning for Dense Audio-Visual Events Localization
Ling Xing, Hongyu Qu, Rui Yan +2
Dense-localization Audio-Visual Events (DAVE) aims to identify time boundaries and corresponding categories for events that are both audible and visible in a long video, where even…
MVP-Shot: Multi-Velocity Progressive-Alignment Framework for Few-Shot Action Recognition
Hongyu Qu, Rui Yan, Xiangbo Shu +3
Recent few-shot action recognition (FSAR) methods typically perform semantic matching on learned discriminative features to achieve promising performance. However, most FSAR method…
Attack-Augmentation Mixing-Contrastive Skeletal Representation Learning
Binqian Xu, Xiangbo Shu, Jiachao Zhang +2
Contrastive learning, relying on effective positive and negative sample pairs, is beneficial to learn informative skeleton representations in unsupervised skeleton-based action rec…
Pyramid Self-attention Polymerization Learning for Semi-supervised Skeleton-based Action Recognition
Binqian Xu, Xiangbo Shu
Most semi-supervised skeleton-based action recognition approaches aim to learn the skeleton action representations only at the joint level, but neglect the crucial motion character…
Spatiotemporal Decouple-and-Squeeze Contrastive Learning for Semi-Supervised Skeleton-based Action Recognition
Binqian Xu, Xiangbo Shu
Contrastive learning has been successfully leveraged to learn action representations for addressing the problem of semi-supervised skeleton-based action recognition. However, most…
Triplet Contrastive Representation Learning for Unsupervised Vehicle Re-identification
Fei Shen, Xiaoyu Du, Liyan Zhang +2
Part feature learning is critical for fine-grained semantic understanding in vehicle re-identification. However, existing approaches directly model part features and global feature…
Dilation-Erosion for Single-Frame Supervised Temporal Action Localization
Bin Wang, Yan Song, Fanming Wang +3
To balance the annotation labor and the granularity of supervision, single-frame annotation has been introduced in temporal action localization. It provides a rough temporal locati…
Wavelet-Attention CNN for Image Classification
Zhao Xiangyu
The feature learning methods based on convolutional neural network (CNN) have successfully produced tremendous achievements in image classification tasks. However, the inherent noi…
Expansion-Squeeze-Excitation Fusion Network for Elderly Activity Recognition
Xiangbo Shu, Jiawen Yang, Rui Yan +1
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 activ…
Interactive Fusion of Multi-level Features for Compositional Activity Recognition
Rui Yan, Lingxi Xie, Xiangbo Shu +1
To understand a complex action, multiple sources of information, including appearance, positional, and semantic features, need to be integrated. However, these features are difficu…
Data-driven Meta-set Based Fine-Grained Visual Classification
Chuanyi Zhang, Yazhou Yao, Xiangbo Shu +3
Constructing fine-grained image datasets typically requires domain-specific expert knowledge, which is not always available for crowd-sourcing platform annotators. Accordingly, lea…
Social Adaptive Module for Weakly-supervised Group Activity Recognition
Rui Yan, Lingxi Xie, Jinhui Tang +2
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…
Spatiotemporal Co-attention Recurrent Neural Networks for Human-Skeleton Motion Prediction
Xiangbo Shu, Liyan Zhang, Guo-Jun Qi +2
Human motion prediction aims to generate future motions based on the observed human motions. Witnessing the success of Recurrent Neural Networks (RNN) in modeling the sequential da…
Region-Manipulated Fusion Networks for Pancreatitis Recognition
Jian Wang, Xiaoyao Li, Xiangbo Shu +1
This work first attempts to automatically recognize pancreatitis on CT scan images. However, different form the traditional object recognition, such pancreatitis recognition is cha…
Hierarchical Long Short-Term Concurrent Memory for Human Interaction Recognition
Xiangbo Shu, Jinhui Tang, Guo-Jun Qi +2
In this paper, we aim to address the problem of human interaction recognition in videos by exploring the long-term inter-related dynamics among multiple persons. Recently, Long Sho…
Deep Ordinal Hashing with Spatial Attention
Lu Jin, Xiangbo Shu, Kai Li +3
Hashing has attracted increasing research attentions in recent years due to its high efficiency of computation and storage in image retrieval. Recent works have demonstrated the su…
Social Anchor-Unit Graph Regularized Tensor Completion for Large-Scale Image Retagging
Jinhui Tang, Xiangbo Shu, Zechao Li +2
Image retagging aims to improve tag quality of social images by refining their original tags or assigning new high-quality tags. Recent approaches simultaneously explore visual, us…
Face Aging with Contextual Generative Adversarial Nets
Si Liu, Yao Sun, Defa Zhu +4
Face aging, which renders aging faces for an input face, has attracted extensive attention in the multimedia research. Recently, several conditional Generative Adversarial Nets (GA…