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- Each Part Matters: Local Patterns Facilitate Cross-View Geo-Localization329
It is argued that neighbor areas can be leveraged as auxiliary information, enriching discriminative clues for geo-localization, and introduced a simple and effective deep neural network, called Local Pattern Network (LPN), to take advantage of contextual information in an end-to-end manner.
- Infrared Small Target Detection with Scale and Location Sensitivity292
A novel Scale and Location Sensitive (SLS) loss is proposed to handle the limitations of existing losses and a simple Multi-Scale Head to the plain U-Net (MSHNet) is designed, which outperforms existing state-of-the-art methods by a large margin.
- Age-Invariant Face Recognition by Multi-Feature Fusionand Decomposition with Self-attention238
A novel Multi-feature Fusion and Decomposition framework for age-invariant face recognition, which learns more discriminative and robust features and reduces the intra-class variants and improves the AIFR performance than previous state-of-the-art methods.
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- Image Demoireing with Learnable Bandpass Filters126
Experimental results on two public datasets show that the novel multiscale bandpass convolutional neural network (MBCNN) outperforms state-of-the-art methods by a large margin.
- Implicit Dual-Domain Convolutional Network for Robust Color Image Compression Artifact Reduction106
An implicit dual-domain convolutional network with a pixel position labeling map and quantization tables as inputs is proposed and is superior to the state-of-the-art methods and IDCN-f exhibits excellent abilities to handle a wide range of compression qualities with a little trade-off against performance.
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- Toward Understanding and Boosting Adversarial Transferability From a Distribution Perspective89
This work proposes a novel method that crafts adversarial examples by manipulating the distribution of the image and can significantly improve the transferability of the crafted attacks and achieves state-of-the-art performance in both untargeted and targeted scenarios.
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- SDPL: Shifting-Dense Partition Learning for UAV-View Geo-Localization78
A dense partition strategy (DPS) is proposed, dividing the image into multiple parts to explore contextual information while explicitly maintaining the global structure, and the shifting-fusion strategy, which generates multiple sets of parts in parallel based on various segmentation centers.
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- AIM 2019 Challenge on Image Demoireing: Methods and Results57
This paper reviews the first-ever image demoireing challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ICCV 2019, and focuses on the proposed solutions and their results.
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- NTIRE 2020 Challenge on Image Demoireing: Methods and Results45
This paper reviews the Challenge on Image Demoireing that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2020, and examines the current state-of-the-art in image and burst image demoireing problems.
- Improving Dynamic HDR Imaging with Fusion Transformer42
This paper proposes a transformer model for HDR imaging that outperforms the state-of-the-art by large margins on several popular public datasets, and demonstrates that the key component is the HDR transformer module.
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- Information-Containing Adversarial Perturbation for Combating Facial Manipulation Systems29
This work proposes a novel two-tier protection method named Information-containing Adversarial Perturbation (IAP), which provides more comprehensive protection for facial images and introduces a feature-level correlation measurement that is more suitable to measure the difference between the facial images than the commonly used mean squared error.
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- Bidirectional difference locating and semantic consistency reasoning for change captioning28
This work proposes a framework with bidirectional difference localization and semantic consistency reasoning to describe the image changes and shows that the performance of the proposed model outperforms the state‐of‐the‐art change captioning models by a large margin.
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- S-Net: a scalable convolutional neural network for JPEG compression artifact reduction25
Experimental results indicate that the proposed S-Net CNN outperforms other CNN-based methods and achieves state-of-the-art performance.
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- Quad Bayer Joint Demosaicing and Denoising Based on Dual Encoder Network with Joint Residual Learning19
A novel dual encoder network, namely DRNet, to achieve joint demosaicing and denoising for Quad Bayer CFA and shows that the proposed method could enhance the reconstruction quality from the native ISP algorithm.
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 2026-10-14. 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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