18 citations · 22 across the 9 of their papers we have counts for
9 papers
What Can I Edit? Open-Ended Strategy Discovery and the Emotion Editability Landscape
Qing Li, Zeyu Dong, Yin Cui +2
Emotional image editing requires more than applying affective filters or modifying predefined visual factors: an effective edit must identify what a particular image can afford for…
Emotion-Qwen: A Unified Framework for Emotion and Vision Understanding
Dawei Huang, Qing Li, Chuan Yan +9
Accurate emotion understanding in videos necessitates effectively recognizing and interpreting emotional states by integrating visual, textual, auditory, and contextual cues. Altho…
3D Landmark Detection on Human Point Clouds: A Benchmark and A Dual Cascade Point Transformer Framework
Fan Zhang, Shuyi Mao, Qing Li +1
3D landmark detection plays a pivotal role in various applications such as 3D registration, pose estimation, and virtual try-on. While considerable success has been achieved in 2D…
IPDAE: Improved Patch-Based Deep Autoencoder for Lossy Point Cloud Geometry Compression
Kang You, Pan Gao, Qing Li
Point cloud is a crucial representation of 3D contents, which has been widely used in many areas such as virtual reality, mixed reality, autonomous driving, etc. With the boost of…
Self-Ensemling for 3D Point Cloud Domain Adaption
Qing Li, Xiaojiang Peng, Chuan Yan +2
Recently 3D point cloud learning has been a hot topic in computer vision and autonomous driving. Due to the fact that it is difficult to manually annotate a qualitative large-scale…
Unsupervised Person Re-Identification with Multi-Label Learning Guided Self-Paced Clustering
Qing Li, Xiaojiang Peng, Yu Qiao +1
Although unsupervised person re-identification (Re-ID) has drawn increasing research attention recently, it remains challenging to learn discriminative features without annotations…
Suppressing Mislabeled Data via Grouping and Self-Attention
Xiaojiang Peng, Kai Wang, Zhaoyang Zeng +3
Deep networks achieve excellent results on large-scale clean data but degrade significantly when learning from noisy labels. To suppressing the impact of mislabeled data, this pape…
Learning Category Correlations for Multi-label Image Recognition with Graph Networks
Qing Li, Xiaojiang Peng, Yu Qiao +1
Multi-label image recognition is a task that predicts a set of object labels in an image. As the objects co-occur in the physical world, it is desirable to model label dependencies…
Product Image Recognition with Guidance Learning and Noisy Supervision
Qing Li, Xiaojiang Peng, Liangliang Cao +3
This paper considers recognizing products from daily photos, which is an important problem in real-world applications but also challenging due to background clutters, category dive…