42 citations · 127 across the 12 of their papers we have counts for
7 papers · 1 filter
AU-Expression Knowledge Constrained Representation Learning for Facial Expression Recognition
Tao Pu, Tianshui Chen, Yuan Xie +2
Recognizing human emotion/expressions automatically is quite an expected ability for intelligent robotics, as it can promote better communication and cooperation with humans. Curre…
Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting
Lingbo Liu, Jiaqi Chen, Hefeng Wu +3
Crowd counting is a fundamental yet challenging task, which desires rich information to generate pixel-wise crowd density maps. However, most previous methods only used the limited…
Knowledge-Guided Multi-Label Few-Shot Learning for General Image Recognition
Tianshui Chen, Liang Lin, Riquan Chen +2
Recognizing multiple labels of an image is a practical yet challenging task, and remarkable progress has been achieved by searching for semantic regions and exploiting label depend…
Adversarial Graph Representation Adaptation for Cross-Domain Facial Expression Recognition
Yuan Xie, Tianshui Chen, Tao Pu +2
Data inconsistency and bias are inevitable among different facial expression recognition (FER) datasets due to subjective annotating process and different collecting conditions. Re…
Fine-Grained Image Captioning with Global-Local Discriminative Objective
Jie Wu, Tianshui Chen, Hefeng Wu +3
Significant progress has been made in recent years in image captioning, an active topic in the fields of vision and language. However, existing methods tend to yield overly general…
Efficient Crowd Counting via Structured Knowledge Transfer
Lingbo Liu, Jiaqi Chen, Hefeng Wu +3
Crowd counting is an application-oriented task and its inference efficiency is crucial for real-world applications. However, most previous works relied on heavy backbone networks a…