6 citations · 11 across the 7 of their papers we have counts for
6 papers · 1 filter
Unsupervised Multi-view Pedestrian Detection
Mengyin Liu, Chao Zhu, Shiqi Ren +1
With the prosperity of the video surveillance, multiple cameras have been applied to accurately locate pedestrians in a specific area. However, previous methods rely on the human-l…
VLPD: Context-Aware Pedestrian Detection via Vision-Language Semantic Self-Supervision
Mengyin Liu, Jie Jiang, Chao Zhu +1
Detecting pedestrians accurately in urban scenes is significant for realistic applications like autonomous driving or video surveillance. However, confusing human-like objects ofte…
Boosting Video Super Resolution with Patch-Based Temporal Redundancy Optimization
Yuhao Huang, Hang Dong, Jinshan Pan +5
The success of existing video super-resolution (VSR) algorithms stems mainly exploiting the temporal information from the neighboring frames. However, none of these methods have di…
Boosting Multi-Modal E-commerce Attribute Value Extraction via Unified Learning Scheme and Dynamic Range Minimization
Mengyin Liu, Chao Zhu, Hongyu Gao +4
With the prosperity of e-commerce industry, various modalities, e.g., vision and language, are utilized to describe product items. It is an enormous challenge to understand such di…
Mutual-Supervised Feature Modulation Network for Occluded Pedestrian Detection
Ye He, Chao Zhu, Xu-Cheng Yin
State-of-the-art pedestrian detectors have achieved significant progress on non-occluded pedestrians, yet they are still struggling under heavy occlusions. The recent occlusion han…
Semantic Bilinear Pooling for Fine-Grained Recognition
Xinjie Li, Chun Yang, Songlu Chen +2
Naturally, fine-grained recognition, e.g., vehicle identification or bird classification, has specific hierarchical labels, where fine categories are always harder to be classified…