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Works399 from public data
- Robust Recovery of Subspace Structures by Low-Rank Representation3,596
It is shown that the convex program associated with LRR solves the subspace clustering problem in the following sense: When the data is clean, LRR exactly recovers the true subspace structures; when the data are contaminated by outliers, it is proved that under certain conditions LRR can exactly recover the row space of the original data.
- Robust Subspace Segmentation by Low-Rank Representation1,756
Both theoretical and experimental results show that low-rank representation is a promising tool for subspace segmentation from corrupted data.
- Tensor Robust Principal Component Analysis with a New Tensor Nuclear Norm1,142
This paper considers the Tensor Robust Principal Component Analysis (TRPCA) problem, which aims to exactly recover the low-rank and sparse components from their sum, and proposes a model based on the recently proposed tensor-tensor product, which includes matrix RPCA as a special case.
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- Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining926
A novel deep network architecture based on deep convolutional and recurrent neural networks for single image deraining based on contextual information is proposed and outperforms the state-of-the-art approaches under all evaluation metrics.
- Expectation-Maximization Attention Networks for Semantic Segmentation700
This paper forms the attention mechanism into an expectation-maximization manner and iteratively estimate a much more compact set of bases upon which the attention maps are computed, which is robust to the variance of input and is also friendly in memory and computation.
- Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization541
This work proves that under certain suitable assumptions, it can recover both the low-rank and the sparse components exactly by simply solving a convex program whose objective is a weighted combination of the tensor nuclear norm and the l1-norm.
- Fast Convex Optimization Algorithms for Exact Recovery of a Corrupted Low-Rank Matrix532
Two complementary approaches for solving the problem of recovering a low-rank matrix with a fraction of its entries arbitrarily corrupted are developed and compared, both several orders of magnitude faster than the previous state-of-the-art algorithm for this problem.
- Accelerated proximal gradient methods for nonconvex programming499
This paper is the first to provide APG-type algorithms for general nonconvex and nonsmooth problems ensuring that every accumulation point is a critical point, and the convergence rates remain O(1/k2) when the problems are convex.
- Subspace Clustering by Block Diagonal Representation472
With this regularizer, the subspace clustering problem is solved by Block Diagonal Representation (BDR), which uses the block diagonal structure prior and the BDR model is nonconvex and the experiments demonstrate the effectiveness of BDR.
- 442
- Fast, automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis438
This paper proposes detecting tampered images by examining the double quantization effect hidden among the discrete cosine transform (DCT) coefficients, and is the only one to date that can automatically locate the tampered region.
- 426
- Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks421
This paper proposes a novel method Relay Backpropagation, which encourages the propagation of effective information through the network in training stage, and achieves the first place in ILSVRC 2015 Scene Classification Challenge.
- 357
- Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models347
Extensive experimental results show that Adan consistently surpasses the corresponding SoTA optimizers on vision, language, and RL tasks and sets new SoTAs for many popular networks and frameworks, e.g., ResNet, ConvNext, ViT, Swin, MAE, DETR, GPT-2, Transformer-XL, and BERT.
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- Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labeled Nodes315
A novel training algorithm for Graph Convolutional Network, called Multi-Stage Self-Supervised (M3S) Training Algorithm, combined with self-supervised learning approach, focusing on improving the generalization performance of GCNs on graphs with few labeled nodes.
- Improving Semantic Segmentation via Decoupled Body and Edge Supervision313
This paper proposes a new paradigm for semantic segmentation that establishes new state of the art while retaining high efficiency in inference and shows that the proposed framework with various baselines or backbone networks leads to better object inner consistency and object boundaries.
- Invertible Image Rescaling306
An Invertible Rescaling Net (IRN) is developed with deliberately designed framework and objectives to produce visually-pleasing low-resolution images and meanwhile capture the distribution of the lost information using a latent variable following a specified distribution in the downscaling process.
- Generalized Nonconvex Nonsmooth Low-Rank Minimization293
In theory, it is proved that IRNN decreases the objective function value monotonically, and any limit point is a stationary point, which enhances the low-rank matrix recovery compared with state-of-the-art convex algorithms.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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