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Works74 from public data
- SwinIR: Image Restoration Using Swin Transformer5,285
A strong baseline model SwinIR is proposed for image restoration based on the Swin Transformer that outperforms state-of-the-art methods on different tasks by up to 0.14∼0.45dB, while the total number of parameters can be reduced byUp to 67%.
- Denoising Diffusion Models for Plug-and-Play Image Restoration511
Experimental results on three representative IR tasks demonstrate that DiffPIR achieves state-of-the-art performance on both the FFHQ and ImageNet datasets in terms of reconstruction faithfulness and perceptual quality with no more than 100 NFEs.
- LocalViT: Analyzing Locality in Vision Transformers460
The locality mechanism is systematically investigated by carefully designed controlled experiments and the same locality mechanism is successfully applied to vision transformers with different architecture designs, which shows the generalization of the locality concept.
- VRT: A Video Restoration Transformer448
Experimental results on five tasks, including video super-resolution, video deblurring, video denoising, video frame interpolation and space-time video super-resolution, demonstrate that VRT outperforms the state-of-the-art methods by large margins on fourteen benchmark datasets.
- Closed-Loop Matters: Dual Regression Networks for Single Image Super-Resolution352
A dual regression scheme is proposed by introducing an additional constraint on LR data to reduce the space of the possible functions and it is shown that this scheme can easily adapt SR models to real-world data, e.g., raw video frames from YouTube.
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- Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis270
Extensive experiments on AGWN removal and real image denoising demonstrate that the new network architecture design achieves state-of-the-art performance and the new degradation model can help to significantly improve the practicability.
- Generative Low-Bitwidth Data Free Quantization161
This paper proposes a knowledge matching generator to produce meaningful fake data by exploiting classification boundary knowledge and distribution information in the pre-trained model with much higher accuracy on 4-bit quantization than the existing data free quantization method.
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- CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-Resolution123
This paper explicitly design an implicit attention network to learn the ensemble weights for the nearby local features in a continuous implicit attention-in-attention network, called CiaoSR, which significantly outperforms the existing single image SR methods with the same backbone.
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- From Whole Slide Imaging to Microscopy: Deep Microscopy Adaptation Network for Histopathology Cancer Image Classification70
A novel Deep Microscopy Adaptation Network (DMAN) is proposed, which can classify MSIs effectively even without MSI annotations, by reducing domain discrepancies via adversarial learning and entropy minimization, and alleviating class imbalance with sample reweighting.
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- Large multimodal models evaluation: a survey57
By unifying general-specialized understanding and modality-specific generation evaluations, this survey clarifies the current landscape and provides guidance for future research in the LMM evaluation field.
- Oversampling for Imbalanced Data via Optimal Transport54
This paper proposes an oversampling method by exploiting global geometric information of data to make synthetic samples follow a similar distribution to that of minority class samples and introduces a novel regularization based on synthetic samples that shifts the distribution of minorityclass samples according to loss information.
- Online Adaptive Asymmetric Active Learning for Budgeted Imbalanced Data52
A novel Online Adaptive Asymmetric Active (OA3) learning algorithm is proposed, based on a new asymmetric strategy (merging both the asymmetric losses and queries strategies), and second-order optimization, which is theoretically analyzed and empirically evaluated on four real-world online anomaly detection tasks.
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- Online Adaptive Asymmetric Active Learning With Limited Budgets31
A novel Online Adaptive Asymmetric Active learning algorithm is proposed, based on a new asymmetric strategy (merging both asymmetric losses and queries strategies), and second-order optimization, which significantly accelerates the computational speed with quite slight performance degradation.
- Learning defense transformations for counterattacking adversarial examples30
This paper empirically verify the existence of defense affine transformations that restore adversarial examples and learns defense transformations to counterattack the adversarialExamples by parameterizing the affine Transformations and exploiting the boundary information of DNNs.
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- Towards Lightweight Super-Resolution With Dual Regression Learning21
A dual regression compression method is proposed to reduce model redundancy in both layer-level and channel-level based on channel pruning to alleviate the first challenge and show the effectiveness of the method in obtaining accurate and efficient SR models.
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Publication data from OpenAlex; 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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