Soomin Seo

Eunjung Cho, Won Ik Cho, Soomin Seo

Hallucination in large language models (LLMs) remains a significant challenge for their safe deployment, particularly due to its potential to spread misinformation. Most existing solutions address this challenge by focusing on aligning the models with credible sources or by improving how models communicate their confidence (or lack thereof) in their outputs. While these measures may be effective in most contexts, they may fall short in scenarios requiring more nuanced approaches, especially in situations where access to accurate data is limited or determining credible sources is challenging. In this study, we take North Korea - a country characterised by an extreme lack of reliable sources and the prevalence of sensationalist falsehoods - as a case study. We explore and evaluate how some of the best-performing multilingual LLMs and specific language-based models generate information about North Korea in three languages spoken in countries with significant geo-political interests: English (United States, United Kingdom), Korean (South Korea), and Mandarin Chinese (China). Our findings reveal significant differences, suggesting that the choice of model and language can lead to vastly different understandings of North Korea, which has important implications given the global security challenges the country poses.

Jaehyup Lee, Soomin Seo, Munchurl Kim

Pan-sharpening is a process of merging a high-resolution (HR) panchromatic (PAN) image and its corresponding low-resolution (LR) multi-spectral (MS) image to create an HR-MS and pan-sharpened image. However, due to the different sensors' locations, characteristics and acquisition time, PAN and MS image pairs often tend to have various amounts of misalignment. Conventional deep-learning-based methods that were trained with such misaligned PAN-MS image pairs suffer from diverse artifacts such as double-edge and blur artifacts in the resultant PAN-sharpened images. In this paper, we propose a novel framework called shift-invariant pan-sharpening with moving object alignment (SIPSA-Net) which is the first method to take into account such large misalignment of moving object regions for PAN sharpening. The SISPA-Net has a feature alignment module (FAM) that can adjust one feature to be aligned to another feature, even between the two different PAN and MS domains. For better alignment in pan-sharpened images, a shift-invariant spectral loss is newly designed, which ignores the inherent misalignment in the original MS input, thereby having the same effect as optimizing the spectral loss with a well-aligned MS image. Extensive experimental results show that our SIPSA-Net can generate pan-sharpened images with remarkable improvements in terms of visual quality and alignment, compared to the state-of-the-art methods.

Soomin Seo, Sehwan Ki, Munchurl Kim

Recently, due to the strength of deep convolutional neural networks (CNN), many CNN-based image quality assessment (IQA) models have been studied. However, previous CNN-based IQA models likely have yet to utilize the characteristics of the human visual system (HVS) fully for IQA problems when they simply entrust everything to the CNN, expecting it to learn from a training dataset. However, in this paper, we propose a novel saliency-channel attention residual network based on the just-noticeable-difference (JND) concept for full-reference image quality assessments (FR-IQA). It is referred to as JND-SalCAR and shows significant improvements in large IQA datasets with various types of distortion. The proposed JND-SalCAR effectively learns how to incorporate human psychophysical characteristics, such as visual saliency and JND, into image quality predictions. In the proposed network, a SalCAR block is devised so that perceptually important features can be extracted with the help of saliency-based spatial attention and channel attention schemes. In addition, a saliency map serves as a guideline for predicting a patch weight map in order to afford stable training of end-to-end optimization for the JND-SalCAR. To the best of our knowledge, our work presents the first HVS-inspired trainable FR-IQA network that considers both visual saliency and the JND characteristics of the HVS. When the visual saliency map and the JND probability map are explicitly given as priors, they can be usefully combined to predict IQA scores rated by humans more precisely, eventually leading to performance improvements and faster convergence. The experimental results show that the proposed JND-SalCAR significantly outperforms all recent state-of-the-art FR-IQA methods on large IQA datasets in terms of the Spearman rank order coefficient (SRCC) and the Pearson linear correlation coefficient (PLCC).