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- Improving 3D Object Detection with Channel-wise Transformer316
This paper leverages the high-quality region proposal network and a Channel-wise Transformer architecture to constitute the two-stage 3D object detection framework (CT3D) with minimal hand-crafted design and achieves superior performance and excellent scalability.
- Multi-Granularity Generator for Temporal Action Proposal216
Through temporally adjusting the segment proposals with fine-grained information based on frame actionness, MGG achieves the superior performance over state-of-the-art methods on the public THUMOS-14 and ActivityNet-1.3 datasets.
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- Single infrared image enhancement using a deep convolutional neural network133
A new refined convolutional neural architecture is designed that produces visually very appealing results with higher contrast and sharper details compared to other network architectures for single infrared image enhancement task.
- Single Infrared Image Optical Noise Removal Using a Deep Convolutional Neural Network115
With a fully convolutional neural network, this method is able to eliminate the optical noise in single infrared image and significant image quality in experiments is achieved compared with the existing method.
- Thermal infrared colorization via conditional generative adversarial network111
This paper proposes a composite loss function that combines content, adversarial, perceptual and total variation losses that is used to recover global image information while the latter three losses are used to synthesize local realistic textures.
- Single Image Super-Resolution Using Compressive Sensing With a Redundant Dictionary95
This paper deals with the SR reconstruction problem based on the theory of compressive sensing, which uses a redundant dictionary instead of a conventional orthogonal basis and demonstrates better results on true images in terms of peak signal-to-noise ratio (PSNR) and root-mean-square error (RMSE) and give several important improvements, compared with other methods.
- Hyperspectral Image Super-Resolution via Dual-Domain Network Based on Hybrid Convolution73
A novel super-resolution (SR) algorithm for HSIs, named SRDNet, is proposed by using a dual-domain network with hybrid convolution and progressive upsampling to exploit both spatial-spectral and frequency information of the hyperspectral data.
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- Improving Pixel-based MIM by Reducing Wasted Modeling Capability49
This paper is the first to systematically investigate multilevel feature fusion for isotropic architectures like the standard Vision Transformer (ViT) and is the first to systematically investigate multilevel feature fusion for isotropic architectures like the standard Vision Transformer (ViT).
- PixMIM: Rethinking Pixel Reconstruction in Masked Image Modeling47
A remarkably simple and effective method that entails filtering the high-frequency components from the reconstruction target to de-emphasize the network's focus on texture-rich details and adopting a conservative data transform strategy to alleviate the problem of missing foreground in MIM training is proposed.
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- Adaptive Contrast Enhancement for Infrared Images Based on the Neighborhood Conditional Histogram42
Qualitative and quantitative evaluation results demonstrate that the proposed method outperforms the other block-based methods on local contrast enhancement, visual quality improvement, and noise suppression.
- Improved generative adversarial network and its application in image oil painting style transfer36
The experimental results show that the WGAN-GPalgorithm constructed in this study has the ability of stable gradient and alternating iterative convergence, and the total variation loss noise constraint can provide good edge and texture details for the migration process of image oil painting style.
- Mitigating and Evaluating Static Bias of Action Representations in the Background and the Foreground35
This paper empirically verify the existence of foreground static bias by creating test videos with conflicting signals from the static and moving portions of the video, and proposes a simple yet effective technique, StillMix, to learn robust action representations.
- FPN estimation based nonuniformity correction for infrared imaging system35
A novel scene-based nonuniformity correction method is proposed based on FPN estimation that leads to the less interference of the details in scenes and the temporarily still scenes, thus the proposed method performs well on the actual scene.
- Research on deep learning garbage classification system based on fusion of image classification and object detection classification30
Practice has proved that the recognition rate of garbage image classification has been increased to about 98%, and it has been transplanted to the raspberry pie microcomputer to achieve ideal results.
- JPEG image steganography payload location based on optimal estimation of cover co-frequency sub-image30
A JPEG image steganography payload location method based on optimal estimation of cover co- frequencies sub-image, which estimates the cover JPEG image based on the Markov model of co-frequency sub- image.
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- Fast pyrolysis of holocellulose for the preparation of long-chain ether fuel precursors: Effect of holocellulose types26
The pyrolysis behaviors of nine biomass-derived holocelluloses were studied and the correlation of sugars/levoglucosan content with a mass ratio of cellulose to hemicellulose (CE/HCE) was put forward.
- Adaptive contrast enhancement based on histogram modification framework26
A novel mapping function is introduced to further improve the enhanced result of histogram equalization (HE) and get better performance on contrast enhancement and visual quality of the enhanced results.
- Optimized Contrast Enhancement for Infrared Images Based on Global and Local Histogram Specification26
Qualitative and quantitative evaluation results demonstrate that the proposed method outperforms the block-based methods for improving the visual quality of infrared images.
- Object Tracking Using Spatio-Temporal Networks for Future Prediction Location24
An object tracking algorithm that predicts the future locations of the target object and assists the tracker to handle object occlusion and sets new state-of-the-art performance on a few commonly used tracking benchmarks.
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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 2026-10-15. 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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