1 citations · 2 across the 4 of their papers we have counts for
5 papers
MIMFlow: Integrating Masked Image Modeling with Normalizing Flows for End-to-End Image Generation
Yang Chen, Xiaowei Xu, Shuai Wang +4
Normalizing Flows (NFs) are powerful generative models capable of exact density estimation and sampling. However, their strict invertibility often forces the model to exhaust its c…
Depth-Copy-Paste: Multimodal and Depth-Aware Compositing for Robust Face Detection
Qiushi Guo
Data augmentation is crucial for improving the robustness of face detection systems, especially under challenging conditions such as occlusion, illumination variation, and complex…
From Competition to Synergy: Unlocking Reinforcement Learning for Subject-Driven Image Generation
Ziwei Huang, Ying Shu, Hao Fang +5
Subject-driven image generation models face a fundamental trade-off between identity preservation (fidelity) and prompt adherence (editability). While online reinforcement learning…
SynRailObs: A Synthetic Dataset for Obstacle Detection in Railway Scenarios
Qiushi Guo, Jason Rambach
Detecting potential obstacles in railway environments is critical for preventing serious accidents. Identifying a broad range of obstacle categories under complex conditions requir…
Enrich the content of the image Using Context-Aware Copy Paste
Qiushi Guo
Data augmentation remains a widely utilized technique in deep learning, particularly in tasks such as image classification, semantic segmentation, and object detection. Among them,…