98 citations · 102 across the 11 of their papers we have counts for
12 papers
GeoLatent: Geometry-Guided Latent Structuring with Routed Optimization for 3D Reasoning
Yakun Zhu, Yi Bin, Yujuan Ding +5
Despite progress in vision-language models, 3D spatial reasoning from 2D images remains challenging. Text-based methods describe intermediate geometry with discrete tokens, limitin…
Physically Plausible Video Generation via Visual-Semantic Chain-of-Events Conditioning
Zixuan Wang, Yixin Hu, Wen Li +4
Physically Plausible Video Generation (PPVG) seeks to synthesize videos consistent with physical principles, yet remains challenging due to underspecified natural language conditio…
Training-free Motion Factorization for Compositional Video Generation
Zixuan Wang, Ziqin Zhou, Feng Chen +4
Compositional video generation aims to synthesize multiple instances with diverse appearance and motion. However, current approaches mainly focus on binding semantics, neglecting t…
Unified Prompt Attack Against Text-to-Image Generation Models
Duo Peng, Qiuhong Ke, Mark He Huang +2
Text-to-Image (T2I) models have advanced significantly, but their growing popularity raises security concerns due to their potential to generate harmful images. To address these is…
Diff-Tracker: Text-to-Image Diffusion Models are Unsupervised Trackers
Zhengbo Zhang, Li Xu, Duo Peng +2
We introduce Diff-Tracker, a novel approach for the challenging unsupervised visual tracking task leveraging the pre-trained text-to-image diffusion model. Our main idea is to leve…
UPAM: Unified Prompt Attack in Text-to-Image Generation Models Against Both Textual Filters and Visual Checkers
Duo Peng, Qiuhong Ke, Jun Liu
Text-to-Image (T2I) models have raised security concerns due to their potential to generate inappropriate or harmful images. In this paper, we propose UPAM, a novel framework that…
Unsupervised Domain Adaptation via Domain-Adaptive Diffusion
Duo Peng, Qiuhong Ke, Yinjie Lei +1
Unsupervised Domain Adaptation (UDA) is quite challenging due to the large distribution discrepancy between the source domain and the target domain. Inspired by diffusion models wh…
Diffusion-based Image Translation with Label Guidance for Domain Adaptive Semantic Segmentation
Duo Peng, Ping Hu, Qiuhong Ke +1
Translating images from a source domain to a target domain for learning target models is one of the most common strategies in domain adaptive semantic segmentation (DASS). However,…
Semantic-Aware Domain Generalized Segmentation
Duo Peng, Yinjie Lei, Munawar Hayat +2
Deep models trained on source domain lack generalization when evaluated on unseen target domains with different data distributions. The problem becomes even more pronounced when we…
Sparse-to-dense Feature Matching: Intra and Inter domain Cross-modal Learning in Domain Adaptation for 3D Semantic Segmentation
Duo Peng, Yinjie Lei, Wen Li +2
Domain adaptation is critical for success when confronting with the lack of annotations in a new domain. As the huge time consumption of labeling process on 3D point cloud, domain…
Global and Local Texture Randomization for Synthetic-to-Real Semantic Segmentation
Duo Peng, Yinjie Lei, Lingqiao Liu +2
Semantic segmentation is a crucial image understanding task, where each pixel of image is categorized into a corresponding label. Since the pixel-wise labeling for ground-truth is…
Hierarchical Paired Channel Fusion Network for Street Scene Change Detection
Yinjie Lei, Duo Peng, Pingping Zhang +2
Street Scene Change Detection (SSCD) aims to locate the changed regions between a given street-view image pair captured at different times, which is an important yet challenging ta…