6 citations · 15 across the 8 of their papers we have counts for
8 papers · 1 filter
Normalizing Batch Normalization for Long-Tailed Recognition
Yuxiang Bao, Guoliang Kang, Linlin Yang +3
In real-world scenarios, the number of training samples across classes usually subjects to a long-tailed distribution. The conventionally trained network may achieve unexpected inf…
Tuning-Free Inversion-Enhanced Control for Consistent Image Editing
Xiaoyue Duan, Shuhao Cui, Guoliang Kang +4
Consistent editing of real images is a challenging task, as it requires performing non-rigid edits (e.g., changing postures) to the main objects in the input image without changing…
Few-Shot Learning with Visual Distribution Calibration and Cross-Modal Distribution Alignment
Runqi Wang, Hao Zheng, Xiaoyue Duan +5
Pre-trained vision-language models have inspired much research on few-shot learning. However, with only a few training images, there exist two crucial problems: (1) the visual feat…
Prompt Tuning Inversion for Text-Driven Image Editing Using Diffusion Models
Wenkai Dong, Song Xue, Xiaoyue Duan +1
Recently large-scale language-image models (e.g., text-guided diffusion models) have considerably improved the image generation capabilities to generate photorealistic images in va…
AttriCLIP: A Non-Incremental Learner for Incremental Knowledge Learning
Runqi Wang, Xiaoyue Duan, Guoliang Kang +5
Continual learning aims to enable a model to incrementally learn knowledge from sequentially arrived data. Previous works adopt the conventional classification architecture, which…
Rethinking the Number of Shots in Robust Model-Agnostic Meta-Learning
Xiaoyue Duan, Guoliang Kang, Runqi Wang +4
Robust Model-Agnostic Meta-Learning (MAML) is usually adopted to train a meta-model which may fast adapt to novel classes with only a few exemplars and meanwhile remain robust to a…
Associative Adversarial Learning Based on Selective Attack
Runqi Wang, Xiaoyue Duan, Baochang Zhang +4
A human's attention can intuitively adapt to corrupted areas of an image by recalling a similar uncorrupted image they have previously seen. This observation motivates us to improv…
Interpretable Attention Guided Network for Fine-grained Visual Classification
Zhenhuan Huang, Xiaoyue Duan, Bo Zhao +2
Fine-grained visual classification (FGVC) is challenging but more critical than traditional classification tasks. It requires distinguishing different subcategories with the inhere…