activity
20122025
most citedGiraffeDet: A Heavy-Neck Paradigm for Object Detection

78 citations · 80 across the 10 of their papers we have counts for

collaborators

10 papers

cs.CV2025

Hierarchical and Step-Layer-Wise Tuning of Attention Specialty for Multi-Instance Synthesis in Diffusion Transformers

Chunyang Zhang, Zhenhong Sun, Zhicheng Zhang +5

Text-to-image (T2I) generation models often struggle with multi-instance synthesis (MIS), where they must accurately depict multiple distinct instances in a single image based on c…

cs.CV2022★ 78 cited

GiraffeDet: A Heavy-Neck Paradigm for Object Detection

Yiqi Jiang, Zhiyu Tan, Junyan Wang +3

In conventional object detection frameworks, a backbone body inherited from image recognition models extracts deep latent features and then a neck module fuses these latent feature…

cs.CV2021

AnANet: Modeling Association and Alignment for Cross-modal Correlation Classification

Nan Xu, Junyan Wang, Yuan Tian +2

The explosive increase of multimodal data makes a great demand in many cross-modal applications that follow the strict prior related assumption. Thus researchers study the definiti…

stat.ME2021★ 1 cited

Empirical Bayes Model Averaging with Influential Observations: Tuning Zellner's g Prior for Predictive Robustness

Christopher M. Hans, Mario Peruggia, Junyan Wang

The behavior of Bayesian model averaging (BMA) for the normal linear regression model in the presence of influential observations that contribute to model misfit is investigated. R…

cs.CV2016

Segmentation Rectification for Video Cutout via One-Class Structured Learning

Junyan Wang, Sai-kit Yeung, Jue Wang +1

Recent works on interactive video object cutout mainly focus on designing dynamic foreground-background (FB) classifiers for segmentation propagation. However, the research on opti…

cs.CV2015★ 1 cited

Superpixelizing Binary MRF for Image Labeling Problems

Junyan Wang, Sai-Kit Yeung

Superpixels have become prevalent in computer vision. They have been used to achieve satisfactory performance at a significantly smaller computational cost for various tasks. Peopl…