43 citations · 131 across the 23 of their papers we have counts for
23 papers
3D Gaussian Splatting: Survey, Technologies, Challenges, and Opportunities
Yanqi Bao, Tianyu Ding, Jing Huo +5
3D Gaussian Splatting (3DGS) has emerged as a prominent technique with the potential to become a mainstream method for 3D representations. It can effectively transform multi-view i…
Training-Free Video Editing via Optical Flow-Enhanced Score Distillation
Lianghan Zhu, Yanqi Bao, Jing Huo +4
The rapid advancement in visual generation, particularly the emergence of pre-trained text-to-image and text-to-video models, has catalyzed growing interest in training-free video…
ONNXPruner: ONNX-Based General Model Pruning Adapter
Dongdong Ren, Wenbin Li, Tianyu Ding +5
Recent advancements in model pruning have focused on developing new algorithms and improving upon benchmarks. However, the practical application of these algorithms across various…
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…
Where and How: Mitigating Confusion in Neural Radiance Fields from Sparse Inputs
Yanqi Bao, Yuxin Li, Jing Huo +4
Neural Radiance Fields from Sparse input} (NeRF-S) have shown great potential in synthesizing novel views with a limited number of observed viewpoints. However, due to the inherent…
InsertNeRF: Instilling Generalizability into NeRF with HyperNet Modules
Yanqi Bao, Tianyu Ding, Jing Huo +3
Generalizing Neural Radiance Fields (NeRF) to new scenes is a significant challenge that existing approaches struggle to address without extensive modifications to vanilla NeRF fra…
Unleashing Realistic Air Quality Forecasting: Introducing the Ready-to-Use PurpleAirSF Dataset
Jingwei Zuo, Wenbin Li, Michele Baldo +1
Air quality forecasting has garnered significant attention recently, with data-driven models taking center stage due to advancements in machine learning and deep learning models. H…
A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniques
Wenbin Li, Hakim Hacid, Ebtesam Almazrouei +1
The union of Edge Computing (EC) and Artificial Intelligence (AI) has brought forward the Edge AI concept to provide intelligent solutions close to the end-user environment, for pr…
A Unified Framework for Contrastive Learning from a Perspective of Affinity Matrix
Wenbin Li, Meihao Kong, Xuesong Yang +4
In recent years, a variety of contrastive learning based unsupervised visual representation learning methods have been designed and achieved great success in many visual tasks. Gen…
Learning Explicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning via Polarization Policy Gradient
Wubing Chen, Wenbin Li, Xiao Liu +2
Cooperative multi-agent policy gradient (MAPG) algorithms have recently attracted wide attention and are regarded as a general scheme for the multi-agent system. Credit assignment…
Modeling Inter-Class and Intra-Class Constraints in Novel Class Discovery
Wenbin Li, Zhichen Fan, Jing Huo +1
Novel class discovery (NCD) aims at learning a model that transfers the common knowledge from a class-disjoint labelled dataset to another unlabelled dataset and discovers new clas…
Uniformly convex neural networks and non-stationary iterated network Tikhonov (iNETT) method
Davide Bianchi, Guanghao Lai, Wenbin Li
We propose a non-stationary iterated network Tikhonov (iNETT) method for the solution of ill-posed inverse problems. The iNETT employs deep neural networks to build a data-driven r…
Tree Structure-Aware Few-Shot Image Classification via Hierarchical Aggregation
Min Zhang, Siteng Huang, Wenbin Li +1
In this paper, we mainly focus on the problem of how to learn additional feature representations for few-shot image classification through pretext tasks (e.g., rotation or color pe…
Playing Lottery Tickets in Style Transfer Models
Meihao Kong, Jing Huo, Wenbin Li +3
Style transfer has achieved great success and attracted a wide range of attention from both academic and industrial communities due to its flexible application scenarios. However,…
Keeping Minimal Experience to Achieve Efficient Interpretable Policy Distillation
Xiao Liu, Shuyang Liu, Wenbin Li +2
Although deep reinforcement learning has become a universal solution for complex control tasks, its real-world applicability is still limited because lacking security guarantees fo…
Manifold Alignment for Semantically Aligned Style Transfer
Jing Huo, Shiyin Jin, Wenbin Li +4
Most existing style transfer methods follow the assumption that styles can be represented with global statistics (e.g., Gram matrices or covariance matrices), and thus address the…
Alleviating the Incompatibility between Cross Entropy Loss and Episode Training for Few-shot Skin Disease Classification
Wei Zhu, Haofu Liao, Wenbin Li +2
Skin disease classification from images is crucial to dermatological diagnosis. However, identifying skin lesions involves a variety of aspects in terms of size, color, shape, and…
Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation
Tiexin Qin, Wenbin Li, Yinghuan Shi +1
Few-shot learning aims to learn a new concept when only a few training examples are available, which has been extensively explored in recent years. However, most of the current wor…
Asymmetric Distribution Measure for Few-shot Learning
Wenbin Li, Lei Wang, Jing Huo +3
The core idea of metric-based few-shot image classification is to directly measure the relations between query images and support classes to learn transferable feature embeddings.…
Semantic Regularization: Improve Few-shot Image Classification by Reducing Meta Shift
Da Chen, Yongliang Yang, Zunlei Feng +6
Few-shot image classification requires the classifier to robustly cope with unseen classes even if there are only a few samples for each class. Recent advances benefit from the met…
Defensive Few-shot Learning
Wenbin Li, Lei Wang, Xingxing Zhang +4
This paper investigates a new challenging problem called defensive few-shot learning in order to learn a robust few-shot model against adversarial attacks. Simply applying the exis…
Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning
Wenbin Li, Lei Wang, Jinglin Xu +3
Few-shot learning in image classification aims to learn a classifier to classify images when only few training examples are available for each class. Recent work has achieved promi…
CariGAN: Caricature Generation through Weakly Paired Adversarial Learning
Wenbin Li, Wei Xiong, Haofu Liao +3
Caricature generation is an interesting yet challenging task. The primary goal is to generate plausible caricatures with reasonable exaggerations given face images. Conventional ca…