2 citations · 5 across the 14 of their papers we have counts for
15 papers
AI-generated Images Challenge Visual Trust in High-risk Scenarios
Yi-Zhi Wang, Yichen Xiao, Linan Yue +5
Rapid advances in image generation are eroding the evidentiary value of visual content in settings where authenticity can affect public safety and personal reputation. Yet existing…
On Aligning Hierarchical Standardized Embedding for Audio-visual Generalized Zero-shot Learning
Zihan Zhang, Jie Hong, Siyuan Fan +2
Audio-visual Generalized Zero-shot Learning (AV-GZSL) is a challenging task that aims to classify both seen and unseen objects or scenes by integrating data from audio and visual m…
EpiAgent: An Agent-Centric System for Ancient Inscription Restoration
Shipeng Zhu, Ang Chen, Na Nie +3
Ancient inscriptions, as repositories of cultural memory, have suffered from centuries of environmental and human-induced degradation. Restoring their intertwined visual and textua…
MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models
Kaixiang Chen, Pengfei Fang, Hui Xue
Adapting large vision-language models (VLMs) such as CLIP to downstream tasks remains challenging, as full fine-tuning is computationally prohibitive and prone to overfitting in lo…
IntervenSim: Intervention-Aware Social Network Simulation for Opinion Dynamics
Yunyao Zhang, Zuocheng Ying, Xinglang Zhang +5
LLM-based social network simulation introduces a new computational approach for modeling event evolution in complex online environments. However, existing methods typically simulat…
Text-Phase Synergy Network with Dual Priors for Unsupervised Cross-Domain Image Retrieval
Jing Yang, Hui Xue, Shipeng Zhu +1
This paper studies unsupervised cross-domain image retrieval (UCDIR), which aims to retrieve images of the same category across different domains without relying on labeled data. E…
SRasP: Self-Reorientation Adversarial Style Perturbation for Cross-Domain Few-Shot Learning
Wenqian Li, Pengfei Fang, Hui Xue
Cross-Domain Few-Shot Learning (CD-FSL) aims to transfer knowledge from a seen source domain to unseen target domains, serving as a key benchmark for evaluating the robustness and…
Adaptive Hyperbolic Kernels: Modulated Embedding in de Branges-Rovnyak Spaces
Leping Si, Meimei Yang, Hui Xue +2
Hierarchical data pervades diverse machine learning applications, including natural language processing, computer vision, and social network analysis. Hyperbolic space, characteriz…
PEARL: Input-Agnostic Prompt Enhancement with Negative Feedback Regulation for Class-Incremental Learning
Yongchun Qin, Pengfei Fang, Hui Xue
Class-incremental learning (CIL) aims to continuously introduce novel categories into a classification system without forgetting previously learned ones, thus adapting to evolving…
On Distilling the Displacement Knowledge for Few-Shot Class-Incremental Learning
Pengfei Fang, Yongchun Qin, Hui Xue
Few-shot Class-Incremental Learning (FSCIL) addresses the challenges of evolving data distributions and the difficulty of data acquisition in real-world scenarios. To counteract th…
SVasP: Self-Versatility Adversarial Style Perturbation for Cross-Domain Few-Shot Learning
Wenqian Li, Pengfei Fang, Hui Xue
Cross-Domain Few-Shot Learning (CD-FSL) aims to transfer knowledge from seen source domains to unseen target domains, which is crucial for evaluating the generalization and robustn…
Towards Few-Shot Learning in the Open World: A Review and Beyond
Hui Xue, Yuexuan An, Yongchun Qin +5
Human intelligence is characterized by our ability to absorb and apply knowledge from the world around us, especially in rapidly acquiring new concepts from minimal examples, under…
Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy
Weijian Mai, Jian Zhang, Pengfei Fang +1
In the era of Artificial Intelligence Generated Content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image, text-to-video, text-to-audio, etc) are gradually…
Text Image Inpainting via Global Structure-Guided Diffusion Models
Shipeng Zhu, Pengfei Fang, Chenjie Zhu +3
Real-world text can be damaged by corrosion issues caused by environmental or human factors, which hinder the preservation of the complete styles of texts, e.g., texture and struct…
PEAN: A Diffusion-Based Prior-Enhanced Attention Network for Scene Text Image Super-Resolution
Zuoyan Zhao, Hui Xue, Pengfei Fang +1
Scene text image super-resolution (STISR) aims at simultaneously increasing the resolution and readability of low-resolution scene text images, thus boosting the performance of the…