8 citations · 20 across the 11 of their papers we have counts for
13 papers
Phased DMD: Few-step Distribution Matching Distillation via Score Matching within Subintervals
Xiangyu Fan, Zesong Qiu, Zhuguanyu Wu +6
Distribution Matching Distillation (DMD) distills score-based generative models into efficient one-step generators, without requiring a one-to-one correspondence with the sampling…
DynAlign: Unsupervised Dynamic Taxonomy Alignment for Cross-Domain Segmentation
Han Sun, Rui Gong, Ismail Nejjar +1
Current unsupervised domain adaptation (UDA) methods for semantic segmentation typically assume identical class labels between the source and target domains. This assumption ignore…
Prompting Diffusion Representations for Cross-Domain Semantic Segmentation
Rui Gong, Martin Danelljan, Han Sun +2
While originally designed for image generation, diffusion models have recently shown to provide excellent pretrained feature representations for semantic segmentation. Intrigued by…
SF-FSDA: Source-Free Few-Shot Domain Adaptive Object Detection with Efficient Labeled Data Factory
Han Sun, Rui Gong, Konrad Schindler +1
Domain adaptive object detection aims to leverage the knowledge learned from a labeled source domain to improve the performance on an unlabeled target domain. Prior works typically…
One-Shot Domain Adaptive and Generalizable Semantic Segmentation with Class-Aware Cross-Domain Transformers
Rui Gong, Qin Wang, Dengxin Dai +1
Unsupervised sim-to-real domain adaptation (UDA) for semantic segmentation aims to improve the real-world test performance of a model trained on simulated data. It can save the cos…
GGViT:Multistream Vision Transformer Network in Face2Face Facial Reenactment Detection
Haotian Wu, Peipei Wang, Xin Wang +2
Detecting manipulated facial images and videos on social networks has been an urgent problem to be solved. The compression of videos on social media has destroyed some pixel detail…
TACS: Taxonomy Adaptive Cross-Domain Semantic Segmentation
Rui Gong, Martin Danelljan, Dengxin Dai +4
Traditional domain adaptive semantic segmentation addresses the task of adapting a model to a novel target domain under limited or no additional supervision. While tackling the inp…
Self-Supervised Pretraining and Controlled Augmentation Improve Rare Wildlife Recognition in UAV Images
Xiaochen Zheng, Benjamin Kellenberger, Rui Gong +2
Automated animal censuses with aerial imagery are a vital ingredient towards wildlife conservation. Recent models are generally based on deep learning and thus require vast amounts…
Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation
Rui Gong, Yuhua Chen, Danda Pani Paudel +5
Open compound domain adaptation (OCDA) is a domain adaptation setting, where target domain is modeled as a compound of multiple unknown homogeneous domains, which brings the advant…
mDALU: Multi-Source Domain Adaptation and Label Unification with Partial Datasets
Rui Gong, Dengxin Dai, Yuhua Chen +2
One challenge of object recognition is to generalize to new domains, to more classes and/or to new modalities. This necessitates methods to combine and reuse existing datasets that…
Self-Calibration Supported Robust Projective Structure-from-Motion
Rui Gong, Danda Pani Paudel, Ajad Chhatkuli +1
Typical Structure-from-Motion (SfM) pipelines rely on finding correspondences across images, recovering the projective structure of the observed scene and upgrading it to a metric…
Analogical Image Translation for Fog Generation
Rui Gong, Dengxin Dai, Yuhua Chen +2
Image-to-image translation is to map images from a given \emph{style} to another given \emph{style}. While exceptionally successful, current methods assume the availability of trai…
DLOW: Domain Flow for Adaptation and Generalization
Rui Gong, Wen Li, Yuhua Chen +1
In this work, we present a domain flow generation(DLOW) model to bridge two different domains by generating a continuous sequence of intermediate domains flowing from one domain to…