activity
20142023
most citedLearning Joint Feature Adaptation for Zero-Shot Recognition

18 citations · 45 across the 11 of their papers we have counts for

collaborators

9 papers

cs.CV20231 cited

PRISE: Demystifying Deep Lucas-Kanade with Strongly Star-Convex Constraints for Multimodel Image Alignment

Yiqing Zhang, Xinming Huang, Ziming Zhang

The Lucas-Kanade (LK) method is a classic iterative homography estimation algorithm for image alignment, but often suffers from poor local optimality especially when image pairs ha…

cs.CV20221 cited

Robust Object Detection With Inaccurate Bounding Boxes

Chengxin Liu, Kewei Wang, Hao Lu +2

Learning accurate object detectors often requires large-scale training data with precise object bounding boxes. However, labeling such data is expensive and time-consuming. As the…

cs.CV20201 cited

TreeRNN: Topology-Preserving Deep GraphEmbedding and Learning

Yecheng Lyu, Ming Li, Xinming Huang +3

General graphs are difficult for learning due to their irregular structures. Existing works employ message passing along graph edges to extract local patterns using customized grap…

cs.CV201618 cited

Learning Joint Feature Adaptation for Zero-Shot Recognition

Ziming Zhang, Venkatesh Saligrama

Zero-shot recognition (ZSR) aims to recognize target-domain data instances of unseen classes based on the models learned from associated pairs of seen-class source and target domai…

cs.CV20164 cited

Real-Time Visual Tracking: Promoting the Robustness of Correlation Filter Learning

Yao Sui, Ziming Zhang, Guanghui Wang +2

Correlation filtering based tracking model has received lots of attention and achieved great success in real-time tracking, however, the lost function in current correlation filter…

cs.CV20144 cited

A Novel Visual Word Co-occurrence Model for Person Re-identification

Ziming Zhang, Yuting Chen, Venkatesh Saligrama

Person re-identification aims to maintain the identity of an individual in diverse locations through different non-overlapping camera views. The problem is fundamentally challengin…