7 papers
STRIDE: Training-Free Diversity Guidance via PCA-Directed Feature Perturbation in Single-Step Diffusion Models
Ankit Yadav, Arpit Garg, Ta Duc Huy +1
Distilled one-step (T=1) or few-step (T4) diffusion models enable real-time image generation but often exhibit reduced sample diversity compared to their multi-step counterpa…
LightAVSeg: Lightweight Audio-Visual Segmentation
Qing Zhong, Guodong Ding, Lingqiao Liu +3
Audio-Visual Segmentation (AVS) targets pixel level localization of sounding emitting objects in videos. However, existing models rely on dense cross-modal attention with quadratic…
A Simple-but-effective Baseline for Training-free Class-Agnostic Counting
Yuhao Lin, Haiming Xu, Lingqiao Liu +1
Class-Agnostic Counting (CAC) seeks to accurately count objects in a given image with only a few reference examples. While previous methods achieving this relied on additional trai…
PP-SSL : Priority-Perception Self-Supervised Learning for Fine-Grained Recognition
ShuaiHeng Li, Qing Cai, Fan Zhang +5
Self-supervised learning is emerging in fine-grained visual recognition with promising results. However, existing self-supervised learning methods are often susceptible to irreleva…
Enhancing Fine-Grained Visual Recognition in the Low-Data Regime Through Feature Magnitude Regularization
Avraham Chapman, Haiming Xu, Lingqiao Liu
Training a fine-grained image recognition model with limited data presents a significant challenge, as the subtle differences between categories may not be easily discernible amids…
On Learning Discriminative Features from Synthesized Data for Self-Supervised Fine-Grained Visual Recognition
Zihu Wang, Lingqiao Liu, Scott Ricardo Figueroa Weston +2
Self-Supervised Learning (SSL) has become a prominent approach for acquiring visual representations across various tasks, yet its application in fine-grained visual recognition (FG…