5 papers · 1 filter
Beyond Class Tokens: LLM-guided Dominant Property Mining for Few-shot Classification
Wei Zhuo, Runjie Luo, Wufeng Xue +1
Few-shot Learning (FSL), which endeavors to develop the generalization ability for recognizing novel classes using only a few images, faces significant challenges due to data scarc…
UINO-FSS: Unifying Representation Learning and Few-shot Segmentation via Hierarchical Distillation and Mamba-HyperCorrelation
Wei Zhuo, Zhiyue Tang, Wufeng Xue +3
Few-shot semantic segmentation has attracted growing interest for its ability to generalize to novel object categories using only a few annotated samples. To address data scarcity,…
HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion
Yu Zeng, Yang Zhang, Jiachen Liu +4
Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g.…
TAVP: Task-Adaptive Visual Prompt for Cross-domain Few-shot Segmentation
Jiaqi Yang, Yaning Zhang, Jingxi Hu +3
While large visual models (LVM) demonstrated significant potential in image understanding, due to the application of large-scale pre-training, the Segment Anything Model (SAM) has…
APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation
Weizhao He, Yang Zhang, Wei Zhuo +4
Few-shot semantic segmentation (FSS) endeavors to segment unseen classes with only a few labeled samples. Current FSS methods are commonly built on the assumption that their traini…