26 citations · 70 across the 12 of their papers we have counts for
13 papers
A Multi-Modal Framework with Cross-Subject Pseudo-Labeling and Semantic Alignment for Micro-Gesture Recognition
Haoran Zhang, Haokun Zhang, Pengyu Liu +3
Micro-gestures (MGs) are spontaneous and subtle body movements that frequently convey hidden human emotions. Recognizing MGs in untrimmed videos remains highly challenging due to t…
Motion Reinforces Appearance: RGB-Skeleton Gated Residual Fusion for Micro-Gesture Online Recognition
Jialin Liu, Xinwen He, Pengyu Liu +3
Micro-gesture analysis attracts increasing attention for inferring spontaneous emotion from subtle body movements. Micro-gesture online recognition, which localizes and classifies…
Selective Volume Mixup for Video Action Recognition
Yi Tan, Zhaofan Qiu, Yanbin Hao +2
The recent advances in Convolutional Neural Networks (CNNs) and Vision Transformers have convincingly demonstrated high learning capability for video action recognition on large da…
CgT-GAN: CLIP-guided Text GAN for Image Captioning
Jiarui Yu, Haoran Li, Yanbin Hao +3
The large-scale visual-language pre-trained model, Contrastive Language-Image Pre-training (CLIP), has significantly improved image captioning for scenarios without human-annotated…
Masked Collaborative Contrast for Weakly Supervised Semantic Segmentation
Fangwen Wu, Jingxuan He, Yufei Yin +3
This study introduces an efficacious approach, Masked Collaborative Contrast (MCC), to highlight semantic regions in weakly supervised semantic segmentation. MCC adroitly draws ins…
Bi-directional Distribution Alignment for Transductive Zero-Shot Learning
Zhicai Wang, Yanbin Hao, Tingting Mu +3
It is well-known that zero-shot learning (ZSL) can suffer severely from the problem of domain shift, where the true and learned data distributions for the unseen classes do not mat…
TKN: Transformer-based Keypoint Prediction Network For Real-time Video Prediction
Haoran Li, XiaoLu Li, Yihang Lin +4
Video prediction is a complex time-series forecasting task with great potential in many use cases. However, traditional methods prioritize accuracy and overlook slow prediction spe…
Long-term Leap Attention, Short-term Periodic Shift for Video Classification
Hao Zhang, Lechao Cheng, Yanbin Hao +1
Video transformer naturally incurs a heavier computation burden than a static vision transformer, as the former processes times longer sequence than the latter under the curren…
Parameterization of Cross-Token Relations with Relative Positional Encoding for Vision MLP
Zhicai Wang, Yanbin Hao, Xingyu Gao +4
Vision multi-layer perceptrons (MLPs) have shown promising performance in computer vision tasks, and become the main competitor of CNNs and vision Transformers. They use token-mixi…
Attention in Attention: Modeling Context Correlation for Efficient Video Classification
Yanbin Hao, Shuo Wang, Pei Cao +4
Attention mechanisms have significantly boosted the performance of video classification neural networks thanks to the utilization of perspective contexts. However, the current rese…
Group Contextualization for Video Recognition
Yanbin Hao, Hao Zhang, Chong-Wah Ngo +1
Learning discriminative representation from the complex spatio-temporal dynamic space is essential for video recognition. On top of those stylized spatio-temporal computational uni…
Token Shift Transformer for Video Classification
Hao Zhang, Yanbin Hao, Chong-Wah Ngo
Transformer achieves remarkable successes in understanding 1 and 2-dimensional signals (e.g., NLP and Image Content Understanding). As a potential alternative to convolutional neur…
Aggregated Multi-GANs for Controlled 3D Human Motion Prediction
Zhenguang Liu, Kedi Lyu, Shuang Wu +3
Human motion prediction from historical pose sequence is at the core of many applications in machine intelligence. However, in current state-of-the-art methods, the predicted futur…