18 papers
Variational Inference for Evidential Deep Learning
Jiawei Tang, Xinyan Du, Hui Liu +2
While Deep Neural Networks (DNNs) achieve remarkable performance, their tendency to produce overconfident predictions. Evidential Deep Learning (EDL) mitigates this by formulating…
DiCaP: Distribution-Calibrated Pseudo-labeling for Semi-Supervised Multi-Label Learning
Bo Han, Zhuoming Li, Xiaoyu Wang +4
Semi-supervised multi-label learning (SSMLL) aims to address the challenge of limited labeled data in multi-label learning (MLL) by leveraging unlabeled data to improve the model's…
ESMC: MLLM-Based Embedding Selection for Explainable Multiple Clustering
Xinyue Wang, Yuheng Jia, Hui Liu +1
Typical deep clustering methods, while achieving notable progress, can only provide one clustering result per dataset. This limitation arises from their assumption of a fixed under…
You Can Trust Your Clustering Model: A Parameter-free Self-Boosting Plug-in for Deep Clustering
Hanyang Li, Yuheng Jia, Hui Liu +1
Recent deep clustering models have produced impressive clustering performance. However, a common issue with existing methods is the disparity between global and local feature struc…
Towards Better IncomLDL: We Are Unaware of Hidden Labels in Advance
Jiecheng Jiang, Jiawei Tang, Jiahao Jiang +3
Label distribution learning (LDL) is a novel paradigm that describe the samples by label distribution of a sample. However, acquiring LDL dataset is costly and time-consuming, whic…
Keep It on a Leash: Controllable Pseudo-label Generation Towards Realistic Long-Tailed Semi-Supervised Learning
Yaxin Hou, Bo Han, Yuheng Jia +2
Current long-tailed semi-supervised learning methods assume that labeled data exhibit a long-tailed distribution, and unlabeled data adhere to a typical predefined distribution (i.…
Graph-based Clustering Revisited: A Relaxation of Kernel -Means Perspective
Wenlong Lyu, Yuheng Jia, Hui Liu +1
The well-known graph-based clustering methods, including spectral clustering, symmetric non-negative matrix factorization, and doubly stochastic normalization, can be viewed as rel…
Structural-Spectral Graph Convolution with Evidential Edge Learning for Hyperspectral Image Clustering
Jianhan Qi, Yuheng Jia, Hui Liu +1
Hyperspectral image (HSI) clustering groups pixels into clusters without labeled data, which is an important yet challenging task. For large-scale HSIs, most methods rely on superp…
Generalization Performance of Ensemble Clustering: From Theory to Algorithm
Xu Zhang, Haoye Qiu, Weixuan Liang +3
Ensemble clustering has demonstrated great success in practice; however, its theoretical foundations remain underexplored. This paper examines the generalization performance of ens…
Acc3D: Accelerating Single Image to 3D Diffusion Models via Edge Consistency Guided Score Distillation
Kendong Liu, Zhiyu Zhu, Hui Liu +1
We present Acc3D to tackle the challenge of accelerating the diffusion process to generate 3D models from single images. To derive high-quality reconstructions through few-step inf…
Mixed Blessing: Class-Wise Embedding guided Instance-Dependent Partial Label Learning
Fuchao Yang, Jianhong Cheng, Hui Liu +3
In partial label learning (PLL), every sample is associated with a candidate label set comprising the ground-truth label and several noisy labels. The conventional PLL assumes the…
PrefPaint: Aligning Image Inpainting Diffusion Model with Human Preference
Kendong Liu, Zhiyu Zhu, Chuanhao Li +3
In this paper, we make the first attempt to align diffusion models for image inpainting with human aesthetic standards via a reinforcement learning framework, significantly improvi…
Irregular Tensor Low-Rank Representation for Hyperspectral Image Representation
Bo Han, Yuheng Jia, Hui Liu +1
Spectral variations pose a common challenge in analyzing hyperspectral images (HSI). To address this, low-rank tensor representation has emerged as a robust strategy, leveraging in…
Learning Efficient and Effective Trajectories for Differential Equation-based Image Restoration
Zhiyu Zhu, Jinhui Hou, Hui Liu +2
The differential equation-based image restoration approach aims to establish learnable trajectories connecting high-quality images to a tractable distribution, e.g., low-quality im…
NVS-Solver: Video Diffusion Model as Zero-Shot Novel View Synthesizer
Meng You, Zhiyu Zhu, Hui Liu +1
By harnessing the potent generative capabilities of pre-trained large video diffusion models, we propose NVS-Solver, a new novel view synthesis (NVS) paradigm that operates \textit…
RainyScape: Unsupervised Rainy Scene Reconstruction using Decoupled Neural Rendering
Xianqiang Lyu, Hui Liu, Junhui Hou
We propose RainyScape, an unsupervised framework for reconstructing clean scenes from a collection of multi-view rainy images. RainyScape consists of two main modules: a neural ren…
Towards Calibrated Deep Clustering Network
Yuheng Jia, Jianhong Cheng, Hui Liu +1
Deep clustering has exhibited remarkable performance; however, the over confidence problem, i.e., the estimated confidence for a sample belonging to a particular cluster greatly ex…
Superpixel Graph Contrastive Clustering with Semantic-Invariant Augmentations for Hyperspectral Images
Jianhan Qi, Yuheng Jia, Hui Liu +1
Hyperspectral images (HSI) clustering is an important but challenging task. The state-of-the-art (SOTA) methods usually rely on superpixels, however, they do not fully utilize the…