Scene Graph Generation (SGG) aims to identify objects and their relationships in visual scenes but faces two key challenges: high computational overhead, particularly for real-time applications, and the long-tailed distribution of predicates, which biases models toward frequent relationships. To address these challenges, we propose Reparams-SGG, a lightweight and efficient network architecture composed of multi-path residual blocks. This architecture reduces computational overhead by leveraging a reparameterization strategy that minimizes sequential and parallel processing, making it highly efficient during inference. Moreover, we introduce a dynamic focal loss that dynamically adjusts the temperature scale during training to focus learning on rare predicates, promoting progressively unbiased learning. Additionally, we propose a dynamic distribution loss, compensating for learning limitations solely from one-hot distributions under data imbalance conditions. We evaluate our method on the widely-used Visual Genome and the recent PSG dataset. Reparams-SGG achieves competitive performance with significantly fewer parameters than state-of-the-art models, demonstrating its efficiency and suitability for deployment in resource-constrained environments.

In this paper, we present new techniques for increasing the diversity of red-teaming prompts generated by automated machine learning-based methods, thereby enabling the discovery of more vulnerabilities in large language models. Using reinforcement learning to train models to output effective prompts for this task results in the models converging deterministically to a single output. Our first technique, which we term Defender, acts by blocking the reward signal for prompts that have already been discovered, thus making what was a stationary problem into a non-stationary problem that compels the reward maximizing algorithm to continually seek new prompts. Our second technique, Teamplay, trains two prompt generation models in tandem and adds the KL divergence between them to the reward in order to make them search in disparate regions of the space of prompts. Our techniques are shown experimentally to increase the effectiveness and diversity of prompts generated by existing reinforcement learning baselines.

Thanks to their long-range dependencies, transformers obtain state-of-the-art performance in diverse research fields such as computer vision and audio processing. In practical scenarios, convolutional neural networks (CNNs) are used more than Transformers due to their low complexity. So, Transformer-to-CNN knowledge distillation (KD) research, where the Transformer is the teacher and the CNN is the student, is in demand and receiving attention. In Transformer-to-CNN KD training, the capacity gap problem arising from structural differences between the teacher and student networks is the main factor of performance degradation of the student network, unlike homogenous architecture KD. However, previous KD studies transfer all of a teacher's knowledge to the student without consid-ering structural differences. They cannot overcome problems caused by structural differences and show poor performance in Transformer-to-CNN KD. In this paper, we iden-tify general and specific knowledge in feature maps of the teacher and student. General and specific knowledge are the generalized and non-generalized feature representation. We propose a novel KD framework DropKD, which extracts general knowledge from the teacher and student while re-moving specific knowledge and then allows general knowledge of the student network to learn general knowledge of the teacher. Our DropKD empowers the student network to achieve generalization by effectively managing general and specific knowledge. Through extensive experiments on challenging image classification datasets, we demonstrate that the proposed method is superior to existing methods.

Recently, deep learning-based facial landmark detection for in-the-wild faces has achieved significant improvement. However, there are still challenges in face landmark detection in other domains (e.g. cartoon, caricature, etc). This is due to the scarcity of extensively annotated training data. To tackle this concern, we design a two-stage training approach that effectively leverages limited datasets and the pre-trained diffusion model to obtain aligned pairs of landmarks and face in multiple domains. In the first stage, we train a landmark-conditioned face generation model on a large dataset of real faces. In the second stage, we fine-tune the above model on a small dataset of image-landmark pairs with text prompts for controlling the domain. Our new designs enable our method to generate high-quality synthetic paired datasets from multiple domains while preserving the alignment between landmarks and facial features. Finally, we fine-tuned a pre-trained face landmark detection model on the synthetic dataset to achieve multi-domain face landmark detection. Our qualitative and quantitative results demonstrate that our method outperforms existing methods on multi-domain face landmark detection.

While generative adversarial network (GAN) models have shown success in generating synthetic data of acoustic, image, and speech, research on generating seismic waves using GAN is receiving great attention. Although some methods have been successful in generating seismic data, they lack the ability to control the generated seismic waves according to earthquake parameters. This letter proposes a novel approach for controllable seismic wave synthesis using auxiliary classifier GAN (ACGAN). Our method focuses on the generation of synthetic seismic waveforms associated with earthquakes of different epicenteral distances. To incorporate distance information into our model, we introduce a distance regression loss function. In addition, we incorporate a feature-level diversity improvement regularization into our model to enhance the diversity of the generated seismic data. The proposed model was trained on KiK-net datasets, and the quality of the generated data was rigorously validated using various validation methods. Experimental results demonstrate the effectiveness of our proposed model in generating seismic waves by adjusting the earthquake epicenter distance.

Estimating earthquake parameters is an essential process for an earthquake analysis system. In particular, the magnitude and epicentral distance of an earthquake are the most basic parameters in earthquake analysis. To estimate these, the existing approaches require long waveform data from multiple stations. In this letter, we propose a novel estimation method based on multitasking deep learning and a convolutional recurrent neural network (CRNN) using only a single station. We also use the stream maximum of the input waveform to accurately estimate the earthquake magnitude. Based on the evaluation using the Stanford Earthquake dataset (STEAD) and the Kiban Kyoshin Network (KiK-net) dataset, we verify the high performance of the proposed method.

This letter proposes a multiple station-based seismic event classification model using a deep convolution neural network (CNN) and graph convolution network (GCN). To classify various seismic events, such as natural earthquakes, artificial earthquakes, and noise, the proposed model consists of weight-shared convolution layers, graph convolution layers, and fully connected layers. We employed graph convolution layers in order to aggregate features from multiple stations. Representative experimental results with the Korean peninsula earthquake datasets from 2016 to 2019 showed that the proposed model is superior to the single-station based state-of the-art methods. Moreover, the proposed model significantly reduced false alarms when using continuous waveforms of long duration. The code is available at.1

In this letter, we propose a learning-based compressive sensing (CS) algorithm for denoising side scan sonar (SSS) images. The proposed method is a deep learning-based CS method with enhanced nonlinearity based on an iterative shrinkage and thresholding algorithm (ISTA). Since noise intensity varies depending on the position within SSS images, the proposed method also incorporates CoordConv, which provides coordinate information to the network to help remove nonhomogeneous noise. Through end-to-end training, both the deep learning module and the CS characteristics can be jointly optimized. Representative experimental results show that the proposed method is better than state-of-art methods in terms of both noise removal and memory requirements.

In this letter, we propose an earthquake event classification model utilizing a feedback network and curriculum learning (CL). In particular, we propose the CL method with a feature concatenation using gated convolution so that CL can be effectively performed in consideration of the feedback structure. We show that the proposed model is effective through comparison experiments with the existing model using the earthquake dataset for Korean Peninsula and the Stanford earthquake dataset.

Recently, most research has been conducted to minimize damage from earthquakes by establishing an early warning system through the analysis of short seismic waves. In particular, deep learning is widely used as it allows to learn complex patterns for earthquake detection from seismic data without complex physical knowledge. In this letter, we propose an improved ConvNetQuake for earthquake event classification by adding learnable features related to the maximum amplitude of the seismic waveform. Since the maximum amplitude is a major factor representing the characteristics of an earthquake, we presented a deep learning structure that can apply this factor in the process of determining whether an earthquake occurs. In the proposed structure, the maximum amplitude is transformed into a feature learned through multi-layer perceptron (MLP) and then concatenates with features extracted through a convolutional neural network (CNN). On the STanford EArthquake Dataset (STEAD) dataset, the proposed method significantly increases the performance for an earthquake event classification than the previous state-of-the-art (SOTA) method by only adding a few parameters.

Image Compressed Sensing (CS) has achieved a lot of performance improvement thanks to advances in deep networks. The CS method is generally composed of a sensing and a decoder. The sensing and decoder networks have a significant impact on the reconstruction performance, and it is obvious that both two networks must be in harmony. However, previous studies have focused on designing the loss function considering only the decoder network. In this paper, we propose a novel training process that can learn sensing and decoder networks simultaneously using Information Bottleneck (IB) theory. By maximizing importance through proposed importance generator, the sensing network is trained to compress important information for image reconstruction of the decoder network. The representative experimental results demonstrate that the proposed method is applied in recently proposed CS algorithms and increases the reconstruction performance with large margin in all CS ratios.

The side-scan sonar (SSS) is a critical sensor device used to explore underwater environments in the deep sea. Gathering SSS data, however, is an expensive and time-consuming task because it requires sensor towing and involves complicated field operations. Recently, deep learning has been making advances rapidly in the field of computer vision. Benefiting from this development, generative adversarial networks (GANs) have been demonstrated to produce realistic synthetic data of various types, including images and acoustics signals. In this letter, we propose a GAN-based semantic image synthesis model based on GAN that can generate high-quality SSS images at a low cost in less time. We evaluate the proposed model using both shallow and deep water SSS data sets that include a diverse range of imaging conditions. such as high and low sonar operating frequencies and different landscapes. The experimental results show that the proposed method can effectively generate synthesized SSS data characterized by the shape and style of real data, thereby demonstrating its promising potential for SSS data augmentation in diverse SSS relevant machine learning tasks.

This letter proposes a multifeature fusion model using deep convolution neural networks and transfer learning approach for earthquake event classification. There are several feature representations for seismic analysis, such as the time domain, the frequency domain, and the time–frequency domain. To successfully classify various earthquake events, we propose a novel model that combines these features hierarchically. In addition, we apply a transfer learning to mitigate overfitting problem of deep learning model while achieving high classification performance. To evaluate our approach, we conduct experiments with the Korean peninsula earthquake database from 2016 to 2018 and a large earthquake database on the Circum-Pacific belt in 2019. The experimental results show that the proposed method outperforms over the compared state-of-the-art methods.

This letter presents a deep convolutional neural network (CNN) with attention module that improves the performance of the classification of various earthquake events. Addressing all possible earthquake events, including not only microearthquakes and artificial-earthquakes but also large-earthquakes, requires both suitable feature expression and a classifier that can effectively discriminate seismic waveforms under adverse conditions. To robustly classify earthquake events, a deep CNN with an attention module was proposed in raw seismic waveforms. Representative experimental results show that the proposed method provides an effective structure for earthquake events classification and, with the Korean peninsula earthquake database from 2016 to 2018, outperforms previous state-of-the-art methods.

This letter proposes an attention-based convolutional neural network architecture for multitasking learning to accurately classify not only the presence of an earthquake but also the event type of the earthquake. In particular, to improve the performance in earthquake-type classification, we develop an attention-based feature aggregation framework embedded in multitask learning architecture. Representative experimental results show that the proposed method provides an effective structure for an earthquake detection and event classification with an earthquake database of the Korean peninsula and the Circum-Pacific belt.

This paper proposes a deep learning method for epicentral distance estimation using a single-channel seismic waveform. The model is based on a convolutional recurrent neural network structure to extract spatial and temporal features. Since the proposed model needs only single-channel data, it can also perform the distance estimation even when some channels of the sensor are adversely disabled. To evaluate our approach, we conduct distance estimation experiments with the Korean peninsula earthquake database from 2016 to 2018, which include microearthquakes and distant earthquakes. The epicentral distance estimation by the proposed method show an absolute mean error of 0.50 km with 9.16km standard deviation of error distribution, which shows the best estimation result among the competing model structures. The promising result indicates that the proposed approach can be deployed for epicentral localization task as part of realizing a robust earthquake monitoring system.

Detecting earthquake events from seismic time series signal is a challenging task. Recently, detection methods based on machine learning have been developed to improve the accuracy and efficiency. However, accuracy of those methods rely on sufficient amount of high-quality training data. In many situations, the high-quality data is difficulty to obtain. We address and resolve this issue by using a Generative Adversarial Network (GAN) model for seismic signal synthesis. GAN already shows its powerful capability in generating high quality synthetic samples in multiple domains. In this paper, we propose a GAN model with gated CNN which can excellently capture sequential structure of seismic time series. We demonstrate its effectiveness via earthquake classification performance. The results show the synthetic data generated by our model indeed can improve the classification performance over the one trained with only real samples.

The image quality of side-scan sonar (SSS) is determined by its operating frequency. SSS operating at a low frequency produces low-quality images due to high levels of noise. This noise is randomly generated from a number of different sources, including equipment noise and underwater environmental interference. In addition, to compensate for transmission loss in a received signal, the signal is amplified by time-varied gain correction, and consequently, SSS images contain nonhomogeneous noise, unlike natural images whose noise is assumed to be homogeneous. In this letter, a structural sparsity-based image denoising algorithm is proposed to remove nonhomogeneous noise from SSS images. The algorithm incorporates both local and nonlocal models in the structural features domain in order to guarantee sparsity and enhance nonlocal self-similarity. Using structural features also preserves fine-scale structures, leading to denoised images with natural seabed textures. The patch weights in the nonlocal model are corrected in consideration of the nonhomogeneity of the noise. Experimental results show that the proposed algorithm is qualitatively and quantitatively comparable to conventional algorithms.

High-frequency surface wave radar (HFSWR) suffers from a man-made interference because its amplitude is high enough to mask the Bragg scattering signal. Although several methods have been proposed for resolving this problem, they are inapplicable to compact HFSWR due to their antenna structures. This letter proposes an effective method of suppressing man-made radio frequency interference for compact HFSWR. The proposed method is composed of man-made interference detection and suppression by using regression based on probabilistic signal model. The proposed method is demonstrated in comparison with conventional methods in terms of root-mean-square error in experiments using synthetic and real data. The results show that the proposed method outperforms other methods in both simulated and practical situations.

This study addresses the automatic multi‐person tracking problem in complex scenes from a single, static, uncalibrated camera. In contrast with offline tracking approaches, a novel online multi‐person tracking method is proposed based on a sequential tracking‐by‐detection framework, which can be applied to real‐time applications. A two‐stage data association is first developed to handle the drifting targets stemming from occlusions and people's abrupt motion changes. Subsequently, a novel online appearance learning is developed by using the incremental/decremental support vector machine with an adaptive training sample collection strategy to ensure reliable data association and rapid learning. Experimental results show the effectiveness and robustness of the proposed method while demonstrating its compatibility with real‐time applications.

Toward a maritime surveillance objective, many ship detection and tracking algorithms have been investigated but are faced with poor performance in practical ocean environments. Compact high-frequency (HF) radar has also faced critical issues due to its long coherent processing interval and varying response from its orthogonal antenna structure. Hence, a simulator based on compact HF radar is proposed in this letter to provide a guideline for effective assessment of ship detection and tracking algorithms while considering these practical issues. To validate the proposed simulator, the simulator generated data has been compared with real data obtained by the compact HF radar sites.

In this paper, a novel methodology is proposed for contrast enhancement and noise reduction in very noisy data with low dynamic range on images captured by surveillance camera under extremely low light condition. For the initial noise reduction, a motion adaptive temporal filtering based on the Kalman filter is employed. Then, the denoised image is first inverted and subsequently dehazed as a tone mapping to enhance the visibility based on the observation that the inverted low light image presents quite similar characteristics to hazy image. Finally, the remaining noise is removed using the Non-local means (NLM) denoising step. The overall approach essentially transforms very dark images progressively into more visible form and effectively reduces the high intensity noise generated by the tone mapping process. From the experimental results, effectiveness of the proposed method is validated by comparing with the most recent and leading conventional method.

This paper proposes a novel combining method of infrared (IR) and visible images based on a Discrete Wavelet Frame (DWF) approach. In contrast to existing methods, IR image is transformed first using statistical information of the visible image to emphasize relevant information. In a multi-scale domain, we then assign appropriate weights to each pixel of sub-band approximation images through pixel level weighted average for emphasizing relevant information of the IR image while keeping texture information of the visible image. Representative experiments show that the proposed method outperforms exiting methods in image quality.

In this paper, we propose a crowd density estimation algorithm based on multi-class Adaboost using spectral texture features. Conventional methods based on self-organizing maps have shown unsatisfactory performance in practical scenarios, and in particular, they have exhibited abrupt degradation in performance under special conditions of crowd densities. In order to address these problems, we have developed a new training strategy by incorporating multi-class Adaboost with spectral texture features that represent a global texture pattern. According to the representative experimental results, the proposed method shows an average improvement of about 30% in the correct recognition rate, as compared to existing conventional methods.

In image reconstruction, dynamic super resolution image reconstruction algorithms have been investigated to enhance video frames sequentially, where explicit motion estimation is considered as a major factor in the performance. This paper proposes a novel measurement validation method to attain robust image reconstruction results under inaccurate motion estimation. In addition, we present an effective scene change detection method dedicated to the proposed super resolution technique for minimizing erroneous results when abrupt scene changes occur in the video frames. Representative experimental results show excellent performance of the proposed algorithm in terms of the reconstruction quality and processing speed.

We address the problem of license plate detection in video surveillance systems. The Adaboost based approach, known for relative ease of implementation, makes use of discriminative features such as edges or Haar-like features. In this paper, we propose a novel detection algorithm based on local structure patterns for license plate detection. The proposed algorithm includes post-processing methods to reduce false positive rate using positional and color information of license plates. Experimental results demonstrate effectiveness of the proposed method compared to both the edge and Haar-like feature based methods.