activity
20212026
most citedTowards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and Baseline

7 citations · 10 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CV2026

SI-Edit: Toward Sketch-Instruction Guided Local Image Editing with Pixel-Level Precision

Weixin Ye, Wei Wang, Hongguang Zhu +1

Despite rapid advances in generative models, achieving pixel-level precision in sketch-based image editing remains a persistent challenge, particularly for fine-grained local defor…

cs.CV2025

Taming Generative Synthetic Data for X-ray Prohibited Item Detection

Jialong Sun, Hongguang Zhu, Weizhe Liu +3

Training prohibited item detection models requires a large amount of X-ray security images, but collecting and annotating these images is time-consuming and laborious. To address d…

cs.CV2025

All-in-One Slider for Attribute Manipulation in Diffusion Models

Weixin Ye, Hongguang Zhu, Wei Wang +3

Text-to-image (T2I) diffusion models have made significant strides in generating high-quality images. However, progressively manipulating certain attributes of generated images to…

cs.CV2025

SAGE: Exploring the Boundaries of Unsafe Concept Domain with Semantic-Augment Erasing

Hongguang Zhu, Yunchao Wei, Mengyu Wang +4

Diffusion models (DMs) have achieved significant progress in text-to-image generation. However, the inevitable inclusion of sensitive information during pre-training poses safety r…

cs.CV2024

BGM: Background Mixup for X-ray Prohibited Items Detection

Weizhe Liu, Renshuai Tao, Hongguang Zhu +3

Current data-driven approaches for X-ray prohibited items detection remain under-explored, particularly in the design of effective data augmentations. Existing natural image augmen…

cs.CV2024

Collaborative Vision-Text Representation Optimizing for Open-Vocabulary Segmentation

Siyu Jiao, Hongguang Zhu, Jiannan Huang +3

Pre-trained vision-language models, e.g. CLIP, have been increasingly used to address the challenging Open-Vocabulary Segmentation (OVS) task, benefiting from their well-aligned vi…