52 citations · 194 across the 36 of their papers we have counts for
22 papers · 1 filter
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…
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.…
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…
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…
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…
Global Structure-Aware Diffusion Process for Low-Light Image Enhancement
Jinhui Hou, Zhiyu Zhu, Junhui Hou +3
This paper studies a diffusion-based framework to address the low-light image enhancement problem. To harness the capabilities of diffusion models, we delve into this intricate pro…
Deep Diversity-Enhanced Feature Representation of Hyperspectral Images
Jinhui Hou, Zhiyu Zhu, Junhui Hou +3
In this paper, we study the problem of efficiently and effectively embedding the high-dimensional spatio-spectral information of hyperspectral (HS) images, guided by feature divers…
Learning A Locally Unified 3D Point Cloud for View Synthesis
Meng You, Mantang Guo, Xianqiang Lyu +2
In this paper, we explore the problem of 3D point cloud representation-based view synthesis from a set of sparse source views. To tackle this challenging problem, we propose a new…
Semi-Supervised Subspace Clustering via Tensor Low-Rank Representation
Yuheng Jia, Guanxing Lu, Hui Liu +1
In this letter, we propose a novel semi-supervised subspace clustering method, which is able to simultaneously augment the initial supervisory information and construct a discrimin…
Content-aware Warping for View Synthesis
Mantang Guo, Junhui Hou, Jing Jin +3
Existing image-based rendering methods usually adopt depth-based image warping operation to synthesize novel views. In this paper, we reason the essential limitations of the tradit…
Deep Attention-guided Graph Clustering with Dual Self-supervision
Zhihao Peng, Hui Liu, Yuheng Jia +1
Existing deep embedding clustering works only consider the deepest layer to learn a feature embedding and thus fail to well utilize the available discriminative information from cl…
Adaptive Attribute and Structure Subspace Clustering Network
Zhihao Peng, Hui Liu, Yuheng Jia +1
Deep self-expressiveness-based subspace clustering methods have demonstrated effectiveness. However, existing works only consider the attribute information to conduct the self-expr…
Learning Dynamic Interpolation for Extremely Sparse Light Fields with Wide Baselines
Mantang Guo, Jing Jin, Hui Liu +1
In this paper, we tackle the problem of dense light field (LF) reconstruction from sparsely-sampled ones with wide baselines and propose a learnable model, namely dynamic interpola…
Semantic-embedded Unsupervised Spectral Reconstruction from Single RGB Images in the Wild
Zhiyu Zhu, Hui Liu, Junhui Hou +2
This paper investigates the problem of reconstructing hyperspectral (HS) images from single RGB images captured by commercial cameras, \textbf{without} using paired HS and RGB imag…
Attention-driven Graph Clustering Network
Zhihao Peng, Hui Liu, Yuheng Jia +1
The combination of the traditional convolutional network (i.e., an auto-encoder) and the graph convolutional network has attracted much attention in clustering, in which the auto-e…
Maximum Entropy Subspace Clustering Network
Zhihao Peng, Yuheng Jia, Hui Liu +2
Deep subspace clustering networks have attracted much attention in subspace clustering, in which an auto-encoder non-linearly maps the input data into a latent space, and a fully c…