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- NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results94
The NTIRE 2022 challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images and the aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics.
- Multi-stage segmentation of white matter hyperintensity, cortical and lacunar infarcts63
An automated multi-stage segmentation approach for labeling the WMH, CI, and LI using multi-modal MR images and the evaluations against the manual labeling and ARWMC visual grading suggest that the algorithm provides reasonable segmentation accuracy.
- Towards Compact Single Image Super-Resolution via Contrastive Self-distillation61
A novel contrastive self-distillation (CSD) framework to simultaneously compress and accelerate various off-the-shelf SR models to improve the quality of SR images and PSNR/SSIM via explicit knowledge transfer is proposed.
- Griffin: Towards a Graph-Centric Relational Database Foundation Model43
Evaluated on large-scale, heterogeneous, and temporal graphs extracted from RDBs across various domains, Griffin demonstrates superior or comparable performance to individually trained models, excels in low-data scenarios, and shows strong transferability with similarity and diversity in pretraining across new datasets and tasks, highlighting its potential as a universally applicable foundation model for RDBs.
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- Slot-VAE: Object-Centric Scene Generation with Slot Attention31
The Slot-VAE is proposed, a generative model that integrates slot attention with the hierarchical VAE framework for object-centric structured scene generation and outperforms slot representation-based generative baselines in terms of sample quality and scene structure accuracy.
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- Do We Really Need Curated Malicious Data for Safety Alignment in Multi-modal Large Language Models?11
Experiments show that, without the need for labor-intensive collection of high-quality malicious data, model safety can still be significantly improved, as long as a specific fraction of rejection data exists in the finetuning set, indicating the security alignment is not lost but rather obscured during multi-modal pretraining or instruction finetuning.
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- Sample Correlation for Fingerprinting Deep Face Recognition5
This work presents SAC-JC, a novel yet simple model stealing detection method based on SAmple Correlation that selects JPEG compressed samples as model inputs and calculates the correlation matrix among their model outputs, and extends it to object recognition datasets including Tiny-ImageNet and CIFAR10.
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- Test-Time Immunization: A Universal Defense Framework Against Jailbreaks for (Multimodal) Large Language Models4
This work proposes a universal defense framework, termed Test-time IMmunization (TIM), which can adaptively defend against various jailbreak attacks in a self-evolving way and implements safety fine-tuning using the detected jailbreak instructions paired with refusal answers.
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- Container Application Migration Algorithm in Internet of Vehicles4
Simulation results show that the dynamic programming method proposed in this paper reduces the long-term average migration total cost by 33.88% and 24.53%, respectively, compared to the nearest selection method and the local optimal method.
- A META-ANALYTIC REVIEW OF SOCIAL MEDIA STUDIES4
The paper systematically reviewed social media studies published in Association for Information Systems (AIS) listed top 20 journals from 2009 to 2013 and formulate projections and recommendations for future social media Studies.
- Discovering anomaly on the basis of flow estimation of alert feature distribution3
The idea of filtering irrelevant alerts based on alert volumes is extended and the flow estimation of abrupt changes in feature distribution caused by anomalies is formulated, by computing Kullback-Leibler distance of alert feature values under observation in comparison with a reference distribution.
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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 2026-10-14. 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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