activity
20162022
most citedStep-Wise Hierarchical Alignment Network for Image-Text Matching

10 citations · 27 across the 9 of their papers we have counts for

collaborators

15 papers

cs.CV2022

Reinforced Pedestrian Attribute Recognition with Group Optimization Reward

Zhong Ji, Zhenfei Hu, Yaodong Wang +1

Pedestrian Attribute Recognition (PAR) is a challenging task in intelligent video surveillance. Two key challenges in PAR include complex alignment relations between images and att…

cs.CV20212 cited

Self-Taught Cross-Domain Few-Shot Learning with Weakly Supervised Object Localization and Task-Decomposition

Xiyao Liu, Zhong Ji, Yanwei Pang +1

The domain shift between the source and target domain is the main challenge in Cross-Domain Few-Shot Learning (CD-FSL). However, the target domain is absolutely unknown during the…

cs.CV202110 cited

Step-Wise Hierarchical Alignment Network for Image-Text Matching

Zhong Ji, Kexin Chen, Haoran Wang

Image-text matching plays a central role in bridging the semantic gap between vision and language. The key point to achieve precise visual-semantic alignment lies in capturing the…

cs.CV2020

Consensus-Aware Visual-Semantic Embedding for Image-Text Matching

Haoran Wang, Ying Zhang, Zhong Ji +2

Image-text matching plays a central role in bridging vision and language. Most existing approaches only rely on the image-text instance pair to learn their representations, thereby…

cs.CV20203 cited

GTNet: Generative Transfer Network for Zero-Shot Object Detection

Shizhen Zhao, Changxin Gao, Yuanjie Shao +4

We propose a Generative Transfer Network (GTNet) for zero shot object detection (ZSD). GTNet consists of an Object Detection Module and a Knowledge Transfer Module. The Object Dete…

cs.CV2019

Episode-based Prototype Generating Network for Zero-Shot Learning

Yunlong Yu, Zhong Ji, Zhongfei Zhang +1

We introduce a simple yet effective episode-based training framework for zero-shot learning (ZSL), where the learning system requires to recognize unseen classes given only the cor…