150 citations · 745 across the 95 of their papers we have counts for
12 papers · 1 filter
Object Style Diffusion for Generalized Object Detection in Urban Scene
Hao Li, Xiangyuan Yang, Mengzhu Wang +4
Object detection is a critical task in computer vision, with applications in various domains such as autonomous driving and urban scene monitoring. However, deep learning-based app…
Revisiting Gradient Normalization and Clipping for Nonconvex SGD under Heavy-Tailed Noise: Necessity, Sufficiency, and Acceleration
Tao Sun, Xinwang Liu, Kun Yuan
Gradient clipping has long been considered essential for ensuring the convergence of Stochastic Gradient Descent (SGD) in the presence of heavy-tailed gradient noise. In this paper…
Active-Passive Federated Learning for Vertically Partitioned Multi-view Data
Jiyuan Liu, Siqi Wang, Xinhang Wan +4
Vertical federated learning is a natural and elegant approach to integrate multi-view data vertically partitioned across devices (clients) while preserving their privacies. Apart f…
DaRec: A Disentangled Alignment Framework for Large Language Model and Recommender System
Xihong Yang, Heming Jing, Zixing Zhang +10
Benefiting from the strong reasoning capabilities, Large language models (LLMs) have demonstrated remarkable performance in recommender systems. Various efforts have been made to d…
Dual Test-time Training for Out-of-distribution Recommender System
Xihong Yang, Yiqi Wang, Jin Chen +5
Deep learning has been widely applied in recommender systems, which has achieved revolutionary progress recently. However, most existing learning-based methods assume that the user…
Part-aware Shape Generation with Latent 3D Diffusion of Neural Voxel Fields
Yuhang Huang, SHilong Zou, Xinwang Liu +1
This paper presents a novel latent 3D diffusion model for the generation of neural voxel fields, aiming to achieve accurate part-aware structures. Compared to existing methods, the…
Test-Time Training on Graphs with Large Language Models (LLMs)
Jiaxin Zhang, Yiqi Wang, Xihong Yang +6
Graph Neural Networks have demonstrated great success in various fields of multimedia. However, the distribution shift between the training and test data challenges the effectivene…
Distilling Reasoning Ability from Large Language Models with Adaptive Thinking
Xiaoshu Chen, Sihang Zhou, Ke Liang +1
Chain of thought finetuning (cot-finetuning) aims to endow small language models (SLM) with reasoning ability to improve their performance towards specific tasks by allowing them t…
Efficient Prompt Tuning of Large Vision-Language Model for Fine-Grained Ship Classification
Long Lan, Fengxiang Wang, Xiangtao Zheng +2
Fine-grained ship classification in remote sensing (RS-FGSC) poses a significant challenge due to the high similarity between classes and the limited availability of labeled data,…
A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges
Wei Ju, Siyu Yi, Yifan Wang +10
Graph-structured data exhibits universality and widespread applicability across diverse domains, such as social network analysis, biochemistry, financial fraud detection, and netwo…
Phrase Grounding-based Style Transfer for Single-Domain Generalized Object Detection
Hao Li, Wei Wang, Cong Wang +4
Single-domain generalized object detection aims to enhance a model's generalizability to multiple unseen target domains using only data from a single source domain during training.…
End-to-end Learnable Clustering for Intent Learning in Recommendation
Yue Liu, Shihao Zhu, Jun Xia +6
Intent learning, which aims to learn users' intents for user understanding and item recommendation, has become a hot research spot in recent years. However, existing methods suffer…