1 citations · 1 across the 6 of their papers we have counts for
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VMonarch: Efficient Video Diffusion Transformers with Structured Attention
Cheng Liang, Haoxian Chen, Liang Hou +4
The quadratic complexity of the attention mechanism severely limits the context scalability of Video Diffusion Transformers (DiTs). We find that the highly sparse spatio-temporal a…
Repulsor: Accelerating Generative Modeling with a Contrastive Memory Bank
Shaofeng Zhang, Xuanqi Chen, Ning Liao +7
The dominance of denoising generative models (e.g., diffusion, flow-matching) in visual synthesis is tempered by their substantial training costs and inefficiencies in representati…
Adapting In-Domain Few-Shot Segmentation to New Domains without Source Domain Retraining
Qi Fan, Kaiqi Liu, Nian Liu +4
Cross-domain few-shot segmentation (CD-FSS) aims to segment objects of novel classes in new domains, which is often challenging due to the diverse characteristics of target domains…
Domain-Rectifying Adapter for Cross-Domain Few-Shot Segmentation
Jiapeng Su, Qi Fan, Guangming Lu +2
Few-shot semantic segmentation (FSS) has achieved great success on segmenting objects of novel classes, supported by only a few annotated samples. However, existing FSS methods oft…
DARNet: Bridging Domain Gaps in Cross-Domain Few-Shot Segmentation with Dynamic Adaptation
Haoran Fan, Qi Fan, Maurice Pagnucco +1
Few-shot segmentation (FSS) aims to segment novel classes in a query image by using only a small number of supporting images from base classes. However, in cross-domain few-shot se…
Selective Feature Adapter for Dense Vision Transformers
Xueqing Deng, Qi Fan, Xiaojie Jin +2
Fine-tuning pre-trained transformer models, e.g., Swin Transformer, are successful in numerous downstream for dense prediction vision tasks. However, one major issue is the cost/st…