28 citations · 103 across the 19 of their papers we have counts for
9 papers · 1 filter
PKI: Prior Knowledge-Infused Neural Network for Few-Shot Class-Incremental Learning
Kexin Baoa, Fanzhao Lin, Zichen Wang +3
Few-shot class-incremental learning (FSCIL) aims to continually adapt a model on a limited number of new-class examples, facing two well-known challenges: catastrophic forgetting a…
Few-shot Class-Incremental Learning via Generative Co-Memory Regularization
Kexin Bao, Yong Li, Dan Zeng +1
Few-shot class-incremental learning (FSCIL) aims to incrementally learn models from a small amount of novel data, which requires strong representation and adaptation ability of mod…
Low-Resolution Object Recognition with Cross-Resolution Relational Contrastive Distillation
Kangkai Zhang, Shiming Ge, Ruixin Shi +1
Recognizing objects in low-resolution images is a challenging task due to the lack of informative details. Recent studies have shown that knowledge distillation approaches can effe…
DANCE: Dual-View Distribution Alignment for Dataset Condensation
Hansong Zhang, Shikun Li, Fanzhao Lin +3
Dataset condensation addresses the problem of data burden by learning a small synthetic training set that preserves essential knowledge from the larger real training set. To date,…
Masked Face Recognition with Generative-to-Discriminative Representations
Shiming Ge, Weijia Guo, Chenyu Li +3
Masked face recognition is important for social good but challenged by diverse occlusions that cause insufficient or inaccurate representations. In this work, we propose a unified…
M3D: Dataset Condensation by Minimizing Maximum Mean Discrepancy
Hansong Zhang, Shikun Li, Pengju Wang +2
Training state-of-the-art (SOTA) deep models often requires extensive data, resulting in substantial training and storage costs. To address these challenges, dataset condensation h…
Private Gradient Estimation is Useful for Generative Modeling
Bochao Liu, Pengju Wang, Weijia Guo +4
While generative models have proved successful in many domains, they may pose a privacy leakage risk in practical deployment. To address this issue, differentially private generati…
Deepfake Video Detection with Spatiotemporal Dropout Transformer
Daichi Zhang, Fanzhao Lin, Yingying Hua +3
While the abuse of deepfake technology has caused serious concerns recently, how to detect deepfake videos is still a challenge due to the high photo-realistic synthesis of each fr…
Bootstrapping Multi-view Representations for Fake News Detection
Qichao Ying, Xiaoxiao Hu, Yangming Zhou +3
Previous researches on multimedia fake news detection include a series of complex feature extraction and fusion networks to gather useful information from the news. However, how cr…