57 citations · 151 across the 13 of their papers we have counts for
13 papers
BeyondFacial: Identity-Preserving Personalized Generation Beyond Facial Close-ups
Songsong Zhang, Chuanqi Tang, Hongguang Zhang +7
Identity-Preserving Personalized Generation (IPPG) has advanced film production and artistic creation, yet existing approaches overemphasize facial regions, resulting in outputs do…
MADS: Multi-Agent Dialogue Simulation for Diverse Persuasion Data Generation
Mingjin Li, Yu Liu, Huayi Liu +4
We propose MADS (Multi-Agent Dialogue Simulation), a scalable framework for generating persuasive multi-turn dialogues via agent self-play. MADS employs three coordinated agents: U…
From Head to Tail: Efficient Black-box Model Inversion Attack via Long-tailed Learning
Ziang Li, Hongguang Zhang, Juan Wang +6
Model Inversion Attacks (MIAs) aim to reconstruct private training data from models, leading to privacy leakage, particularly in facial recognition systems. Although many studies h…
Exploiting Inter-sample and Inter-feature Relations in Dataset Distillation
Wenxiao Deng, Wenbin Li, Tianyu Ding +5
Dataset distillation has emerged as a promising approach in deep learning, enabling efficient training with small synthetic datasets derived from larger real ones. Particularly, di…
Multi-level Second-order Few-shot Learning
Hongguang Zhang, Hongdong Li, Piotr Koniusz
We propose a Multi-level Second-order (MlSo) few-shot learning network for supervised or unsupervised few-shot image classification and few-shot action recognition. We leverage so-…
Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot Learning
Hongguang Zhang, Piotr Koniusz, Songlei Jian +2
The majority of existing few-shot learning methods describe image relations with binary labels. However, such binary relations are insufficient to teach the network complicated rea…
Improving Few-shot Learning by Spatially-aware Matching and CrossTransformer
Hongguang Zhang, Philip H. S. Torr, Piotr Koniusz
Current few-shot learning models capture visual object relations in the so-called meta-learning setting under a fixed-resolution input. However, such models have a limited generali…
Few-Shot Learning via Saliency-guided Hallucination of Samples
Hongguang Zhang, Jing Zhang, Piotr Koniusz
Learning new concepts from a few of samples is a standard challenge in computer vision. The main directions to improve the learning ability of few-shot training models include (i)…
Deep Stacked Hierarchical Multi-patch Network for Image Deblurring
Hongguang Zhang, Yuchao Dai, Hongdong Li +1
Despite deep end-to-end learning methods have shown their superiority in removing non-uniform motion blur, there still exist major challenges with the current multi-scale and scale…
Power Normalizing Second-order Similarity Network for Few-shot Learning
Hongguang Zhang, Piotr Koniusz
Second- and higher-order statistics of data points have played an important role in advancing the state of the art on several computer vision problems such as the fine-grained imag…
Model Selection for Generalized Zero-shot Learning
Hongguang Zhang, Piotr Koniusz
In the problem of generalized zero-shot learning, the datapoints from unknown classes are not available during training. The main challenge for generalized zero-shot learning is th…
Museum Exhibit Identification Challenge for Domain Adaptation and Beyond
Piotr Koniusz, Yusuf Tas, Hongguang Zhang +3
In this paper, we approach an open problem of artwork identification and propose a new dataset dubbed Open Museum Identification Challenge (Open MIC). It contains photos of exhibit…
Zero-Shot Kernel Learning
Hongguang Zhang, Piotr Koniusz
In this paper, we address an open problem of zero-shot learning. Its principle is based on learning a mapping that associates feature vectors extracted from i.e. images and attribu…