Instruction tuning plays a critical role in enhancing the performance and efficiency of Large Language Models (LLMs). Its success depends not only on the quality of the instruction data but also on the inherent capabilities of the LLM itself. Some studies suggest that even a small amount of high-quality data can achieve instruction fine-tuning results that are on par with, or even exceed, those from using a full-scale dataset. However, rather than focusing solely on calculating data quality scores to evaluate instruction data, there is a growing need to select high-quality data that maximally enhances the performance of instruction tuning for a given LLM. In this paper, we propose the Model Instruction Weakness Value (MIWV) as a novel metric to quantify the importance of instruction data in enhancing model's capabilities. The MIWV metric is derived from the discrepancies in the model’s responses when using In-Context Learning (ICL), helping identify the most beneficial data for enhancing instruction tuning performance. Our experimental results demonstrate that selecting only the top 1% of data based on MIWV can outperform training on the full dataset. Furthermore, this approach extends beyond existing research that focuses on data quality scoring for data selection, offering strong empirical evidence supporting the effectiveness of our proposed method.

Spatiotemporal fusion (STF) is an effective solution to promote the application of remote sensing images, given that the tradeoff between the temporal resolution and the spatial resolution is ubiquitous in the production of remote sensing images. However, cloud coverage makes it difficult to obtain dense cloud-free Landsat–Moderate Resolution Imaging Spectroradiometer (MODIS) image pairs on the timeline, which limits the application of existing STF methods. Considering the lack of prior image pairs and the huge spatial resolution gap between Landsat and MODIS images, this article presents a novel remote sensing image STF method based on a generative adversarial network to handle one Landsat–MODIS prior image pair case (OPGAN), which contains a generator and a discriminator simultaneously trained in a min–max game. OPGAN is built based on the STF observation model that learns the base information from the prior Landsat image and then captures temporal change (TC) information from a difference image constructed from MODIS images collected at times 1 and 2 and sensor difference information from the difference image between Landsat and MODIS images at time 1. They are combined together to reconstruct the Landsat image at time 2 at both high spatial and high temporal resolution. Moreover, a change loss is proposed to further improve the accuracy of TC prediction. Extensive experiments on the STF dataset illustrate that the proposed OPGAN method can obtain more accurate prediction of spatial information and TCs in the case of insufficient prior information.

Due to technological limitations and budget constraints, spatiotemporal fusion is considered a promising way to deal with the tradeoff between the temporal and spatial resolutions of remote sensing images. Furthermore, the generative adversarial network (GAN) has shown its capability in a variety of applications. This article presents a remote sensing image spatiotemporal fusion method using a GAN (STFGAN), which adopts a two-stage framework with an end-to-end image fusion GAN (IFGAN) for each stage. The IFGAN contains a generator and a discriminator in competition with each other under the guidance of the optimization function. Considering the huge spatial resolution gap between the high-spatial, low-temporal (HSLT) resolution Landsat imagery and the corresponding low-spatial, high-temporal (LSHT) resolution MODIS imagery, a feature-level fusion strategy is adopted. Specifically, for the generator, we first super-resolve the MODIS images while also extracting the high-frequency features of the Landsat images. Finally, we integrate the features from the MODIS and Landsat images. STFGAN is able to learn an end-to-end mapping between the Landsat-MODIS image pairs and predicts the Landsat-like image for a prediction date by considering all the bands. STFGAN significantly improves the accuracy of phenological change and land-cover-type change prediction with the help of residual blocks and two prior Landsat-MODIS image pairs. To examine the performance of the proposed STFGAN method, experiments were conducted on three representative Landsat-MODIS data sets. The results clearly illustrate the effectiveness of the proposed method.

Spatio-temporal fusion is a promising way to deal with the tradeoff between the temporal resolution and spatial resolution of the remote sensing images. This paper presents a novel remote sensing image spatio-temporal fusion model to expand the application of spatio-temporal fusion with insufficient data, based on a generative adversarial network to handle one prior image pair cases (OPGAN). Considering the huge spatial resolution gap between the high-spatial, low-temporal (HSLT) resolution Landsat imagery and the corresponding low-spatial, high-temporal (LSHT) resolution MODIS imagery, the proposed OPGAN simultaneously trains a generator and a discriminator in a min-max game to reconstruct the high-spatial-high-temporal (HSHT) resolution Landsat images, significantly improving the accuracy of change prediction with the help of the temporal changes and sensor differences. Experimental results on three representative Landsat-MODIS datasets illustrate the effectiveness of the proposed OPGAN method.