Nighttime light (NTL) remote sensing observation serves as a unique proxy for quantitatively assessing progress toward meeting a series of Sustainable Development Goals (SDGs), such as poverty estimation, urban sustainable development, and carbon emission. However, existing NTL observations often suffer from pervasive degradation and inconsistency, limiting their utility for computing the indicators defined by the SDGs. In this study, we propose a novel approach to reconstruct high-resolution NTL images using multi-modal remote sensing data. To support this research endeavor, we introduce DeepLightMD, a comprehensive dataset comprising data from five heterogeneous sensors, offering fine spatial resolution and rich spectral information at a national scale. Additionally, we present DeepLightSR, a calibration-aware method for building bridges between spatially heterogeneous modality data in the multi-modality super-resolution. DeepLightSR integrates calibration-aware alignment, an auxiliary-to-main multi-modality fusion, and an auxiliary-embedded refinement to effectively address spatial heterogeneity, fuse diversely representative features, and enhance performance in $8\times$ super-resolution (SR) tasks. Extensive experiments demonstrate the superiority of DeepLightSR over 8 competing methods, as evidenced by improvements in PSNR (2.01 dB $ \sim $ 13.25 dB) and PIQE (0.49 $ \sim $ 9.32). Our findings underscore the practical significance of our proposed dataset and model in reconstructing high-resolution NTL data, supporting efficiently and quantitatively assessing the SDG progress.
CycleGAN has been proven to be an advanced approach for unsupervised image restoration. This framework consists of two generators: a denoising one for inference and an auxiliary one for modeling noise to fulfill cycle-consistency constraints. However, when applied to the infrared destriping task, it becomes challenging for the vanilla auxiliary generator to consistently produce vertical noise under unsupervised constraints. This poses a threat to the effectiveness of the cycle-consistency loss, leading to stripe noise residual in the denoised image. To address the above issue, we present a novel framework for single-frame infrared image destriping, named DestripeCycleGAN. In this model, the conventional auxiliary generator is replaced with a priori stripe generation model (SGM) to introduce vertical stripe noise in the clean data, and the gradient map is employed to re-establish cycle-consistency. Meanwhile, a Haar wavelet background guidance module (HBGM) has been designed to minimize the divergence of background details between the different domains. To preserve vertical edges, a multi-level wavelet U-Net (MWUNet) is proposed as the denoising generator, which utilizes the Haar wavelet transform as the sampler to decline directional information loss. Moreover, it incorporates the group fusion block (GFB) into skip connections to fuse the multi-scale features and build the context of long-distance dependencies. Extensive experiments on real and synthetic data demonstrate that our DestripeCycleGAN surpasses the state-of-the-art methods in terms of visual quality and quantitative evaluation. Our code will be made public at https://github.com/0wuji/DestripeCycleGAN.
Although binary classification is a well-studied problem in computer vision, training reliable classifiers under severe class imbalance remains a challenging problem. Recent work has proposed techniques that mitigate the effects of training under imbalance by modifying the loss functions or optimization methods. While this work has led to significant improvements in the overall accuracy in the multi-class case, we observe that slight changes in hyperparameter values of these methods can result in highly variable performance in terms of Receiver Operating Characteristic (ROC) curves on binary problems with severe imbalance. To reduce the sensitivity to hyperparameter choices and train more general models, we propose training over a family of loss functions, instead of a single loss function. We develop a method for applying Loss Conditional Training (LCT) to an imbalanced classification problem. Extensive experiment results, on both CIFAR and Kaggle competition datasets, show that our method improves model performance and is more robust to hyperparameter choices. Code will be made available at: https://github.com/klieberman/roc_lct.
The lottery ticket hypothesis posits the existence of ``winning tickets'' within a randomly initialized neural network. Do winning tickets exist for LLMs in fine-tuning scenarios? How can we find such winning tickets? In this paper, we propose KS-Lottery, a method to identify a small subset of LLM parameters highly effective in multilingual fine-tuning. Our key idea is to use Kolmogorov-Smirnov Test to analyze the distribution shift of parameters before and after fine-tuning. We further theoretically prove that KS-Lottery can find the certified winning tickets in the embedding layer, fine-tuning on the found parameters is guaranteed to perform as well as full fine-tuning. Comparing KS-Lottery with other parameter-efficient tuning algorithms on translation tasks, the experimental results show that KS-Lottery finds a much smaller set of parameters for fine-tuning while achieving the comparable performance as full fine-tuning LLM. Surprisingly, we find that fine-tuning 18 tokens' embedding of LLaMA suffices to reach the fine-tuning translation performance. Code and model will be released to the public.
Infrared small target detection (IRSTD) has recently benefitted greatly from U-shaped neural models. However, largely overlooking effective global information modeling, existing techniques struggle when the target has high similarities with the background. We present a Spatial-channel Cross Transformer Network (SCTransNet) that leverages spatial-channel cross transformer blocks (SCTBs) on top of long-range skip connections to address the aforementioned challenge. In the proposed SCTBs, the outputs of all encoders are interacted with cross transformer to generate mixed features, which are redistributed to all decoders to effectively reinforce semantic differences between the target and clutter at full scales. Specifically, SCTB contains the following two key elements: (a) spatial-embedded single-head channel-cross attention (SSCA) for exchanging local spatial features and full-level global channel information to eliminate ambiguity among the encoders and facilitate high-level semantic associations of the images, and (b) a complementary feed-forward network (CFN) for enhancing the feature discriminability via a multi-scale strategy and cross-spatial-channel information interaction to promote beneficial information transfer. Our SCTransNet effectively encodes the semantic differences between targets and backgrounds to boost its internal representation for detecting small infrared targets accurately. Extensive experiments on three public datasets, NUDT-SIRST, NUAA-SIRST, and IRSTD-1k, demonstrate that the proposed SCTransNet outperforms existing IRSTD methods. Our code will be made public at https://github.com/xdFai.
Infrared image destriping seeks to restore high-quality content from degraded images. Recent works mainly address this task by leveraging prior knowledge to separate stripe noise from the degraded image. However, constructing a robust decoupling model for that purpose remains challenging, especially when significant similarities exist between the stripe noise and vertical background structure. Addressing that, we introduce Asymmetric Residual wavelet Column correction Network (ARCNet) for image destriping, aiming to consistently preserve spatially precise high-resolution representations. Our neural model leverages a novel downsampler, residual haar discrete wavelet transform (RHDWT), stripe directional prior knowledge and data-driven learning to induce a model with enriched feature representation of stripe noise and background. In our technique, the inverse wavelet transform is replaced by transposed convolution for feature upsampling, which can suppress noise crosstalk and encourage the network to focus on robust image reconstruction. After each sampling, a proposed column non-uniformity correction module (CNCM) is leveraged by our method to enhance column uniformity, spatial correlation, and global self-dependence between each layer component. CNCM can establish structural characteristics of stripe noise and utilize contextual information at long-range dependencies to distinguish stripes with varying intensities and distributions. Extensive experiments on synthetic data, real data, and infrared small target detection tasks show that the proposed method outperforms state-of-the-art single-image destriping methods both visually and quantitatively by a considerable margin. Our code will be made publicly available at \url{https://github.com/xdFai}.
Large Language Models (LLMs) have greatly propelled the progress in natural language(NL)-centric tasks based on NL interface. However, the NL form is not enough for world knowledge. Current works focus on this question by injecting specific symbolic knowledge into LLM, which ignore two critical challenges: the interrelations between various symbols and the balance between symbolic-centric and NL-centric capabilities. In this work, we tackle these challenges from both a data and framework perspective and introduce Symbol-LLM series models. First, we collect 34 symbolic tasks, covering ~20 different forms, which are unified to capture symbol interrelations. Then, a two-stage tuning framework succeeds in injecting symbolic knowledge without loss of the generality ability. Extensive experiments on both symbol- and NL-centric tasks demonstrate the balanced and superior performances of Symbol-LLM series models.
Large Language Models (LLMs), trained predominantly on extensive English data, often exhibit limitations when applied to other languages. Current research is primarily focused on enhancing the multilingual capabilities of these models by employing various tuning strategies. Despite their effectiveness in certain languages, the understanding of the multilingual abilities of LLMs remains incomplete. This study endeavors to evaluate the multilingual capacity of LLMs by conducting an exhaustive analysis across 101 languages, and classifies languages with similar characteristics into four distinct quadrants. By delving into each quadrant, we shed light on the rationale behind their categorization and offer actionable guidelines for tuning these languages. Extensive experiments reveal that existing LLMs possess multilingual capabilities that surpass our expectations, and we can significantly improve the multilingual performance of LLMs by focusing on these distinct attributes present in each quadrant.
Powered by the advances of optical remote sensing sensors, the production of very high spatial resolution multispectral images provides great potential for achieving cost-efficient and high-accuracy forest inventory and analysis in an automated way. Lots of studies that aim at providing an inventory to the level of each individual tree have generated a variety of methods for Individual Tree Crown Detection and Delineation (ITCD). This review covers ITCD methods for detecting and delineating individual tree crowns, and systematically reviews the past and present of ITCD-related researches applied to the optical remote sensing images. With the goal to provide a clear knowledge map of existing ITCD efforts, we conduct a comprehensive review of recent ITCD papers to build a meta-data analysis, including the algorithm, the study site, the tree species, the sensor type, the evaluation method, etc. We categorize the reviewed methods into three classes: (1) traditional image processing methods (such as local maximum filtering, image segmentation, etc.); (2) traditional machine learning methods (such as random forest, decision tree, etc.); and (3) deep learning based methods. With the deep learning-oriented approaches contributing a majority of the papers, we further discuss the deep learning-based methods as semantic segmentation and object detection methods. In addition, we discuss four ITCD-related issues to further comprehend the ITCD domain using optical remote sensing data, such as comparisons between multi-sensor based data and optical data in ITCD domain, comparisons among different algorithms and different ITCD tasks, etc. Finally, this review proposes some ITCD-related applications and a few exciting prospects and potential hot topics in future ITCD research.
Both optical flow and stereo disparities are image matches and can therefore benefit from joint training. Depth and 3D motion provide geometric rather than photometric information and can further improve optical flow. Accordingly, we design a first network that estimates flow and disparity jointly and is trained without supervision. A second network, trained with optical flow from the first as pseudo-labels, takes disparities from the first network, estimates 3D rigid motion at every pixel, and reconstructs optical flow again. A final stage fuses the outputs from the two networks. In contrast with previous methods that only consider camera motion, our method also estimates the rigid motions of dynamic objects, which are of key interest in applications. This leads to better optical flow with visibly more detailed occlusions and object boundaries as a result. Our unsupervised pipeline achieves 7.36% optical flow error on the KITTI-2015 benchmark and outperforms the previous state-of-the-art 9.38% by a wide margin. It also achieves slightly better or comparable stereo depth results. Code will be made available.