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- Maojun ZhangSuggested from co-authorship
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Works62 from public data
- Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi-Supervised Semantic Segmentation627
It is found that varying dilation rates can effectively enlarge the receptive fields of convolutional kernels and more importantly transfer the surrounding discriminative information to non-discriminative object regions, promoting the emergence of these regions in the object localization maps.
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- Accelerating thrombolysis using a precision and clot-penetrating drug delivery strategy by nanoparticle-shelled microbubbles194
A porous and magnetic microbubble platform is developed to formulate tPA and this strategy not only improves the therapeutic efficacy but also accelerates the lytic rate, enabling it to be promising in time-critical thrombolytic therapy.
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- Video Scene Parsing with Predictive Feature Learning117
It is experimentally proved that the learned predictive features in the model are able to significantly enhance the video parsing performance by combining with the standard image parsing network.
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- A Class Imbalance Loss for Imbalanced Object Recognition50
A method for generating an imbalanced few-shot learning dataset and the effectiveness of an algorithm for model-agnostic metalearning for rapid adaptation with CI loss are presented.
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- NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results35
The 1st NTIRE challenge on stereo image super-resolution (restoration of rich details in a pair of low-resolution stereo images) with a focus on new solutions and results establishes a new benchmark for stereo image SR.
- Efficient Long-Short Temporal Attention network for unsupervised Video Object Segmentation28
An efficient Long-Short Temporal Attention network (termed LSTA) is developed for unsupervised VOS task from a holistic view to achieve nearly linear time complexity for the two light modules.
- Transferable Semi-Supervised Semantic Segmentation27
A novel transferable semi-supervised semantic segmentation model that can transfer the learned segmentation knowledge from a few strong categories with pixel- level annotations to unseen weak categories with only image-level annotations is proposed, significantly broadening the applicable territory of deep segmentation models.
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- Pixelwise Estimation of Signal-Dependent Image Noise Using Deep Residual Learning19
This paper proposes a deep convolutional neural network named “deep residual noise estimator” (DRNE) for pixelwise noise-level estimation and carefully design the architecture of the DRNE, which consists of a stack of customized residual blocks without any pooling or interpolation operation.
- ThermalGS: Dynamic 3D Thermal Reconstruction with Gaussian Splatting18
This work proposes a novel dynamic thermal 3D reconstruction method, named ThermalGS, based on 3D Gaussian Splatting (3DGS), which employs a data-driven approach to directly learn both scene structure and dynamic thermal representation, using RGB and TIR images as input.
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- A photo-responsive self-healing hydrogel loaded with immunoadjuvants and MoS 2 nanosheets for combating post-resection breast cancer recurrence14
An implantable photo-responsive self-healing hydrogel loaded with MoS2 nanosheets and the immunoadjuvant R837 for in situ generation of tumor-associated antigens at the post-surgical site of the primary tumor, enabling sustained and effective activation of the immune response.
- A nanoparticle-containing polycaprolactone implant for combating post-resection breast cancer recurrence12
This strategy using implantable drug-containing polymeric scaffolds to deliver chemo drugs directly to the post-resection site, and simultaneously provide mechanical support and regenerative niche for breast tissue reconstruction holds great promise as an adjuvant localized chemotherapy in clinics for combating post- resection breast cancer recurrence.
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- MSBA: Multiple Scales, Branches and Attention Network With Bag of Tricks for Person Re-Identification10
A model that combines the most effective designs, including multi-scale, multi-branch and attention mechanism, and report training tricks that are no less or even more important in improving person Re-ID performance is proposed.
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- Denoising real bursts with squeeze‐and‐excitation residual network6
The proposed deep residual model with squeeze-and-excitation modules for the burst denoising performs significantly better than previous state-of-the-art methods V-BM4D and KPN in removing Gaussian noise and is effective in removing real noise using bursts.
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 2026-10-15. 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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