Search

As generative models become more advanced and essential, interest in using these models for image editing is growing. Nevertheless, conventional image editing methods still have many problems and can alter the intrinsic properties (e.g. geometry) of an object during the editing process. In this article, a novel geometry‐preserved image editing method is presented, where key points, keyframe, or canny edges are utilized as inputs for geometric constraints. A text prompt is fed into the inversion pipeline along with the input image and key points/keyframes/canny for editing. Then, new images are generated based on the editing pipeline of the denoising diffusion implicit model. Experiments demonstrate that the method outperforms its baselines. [ABSTRACT FROM AUTHOR]

We propose a novel integral probability metric-based generative adversarial network (GAN), called SphereGAN. In the proposed scheme, the distance between two probability distributions (i.e., true and fake distributions) is measured on a hypersphere. Given that its hypersphere-based objective function computes the upper bound of the distance as a half arc, SphereGAN can be stably trained and can achieve a high convergence rate. In sphereGAN, higher-order information of data is processed using multiple geometric moments, thus improving the accuracy of the distance measurement and producing more realistic outcomes. Several properties of the proposed distance metric on the hypersphere are mathematically derived. The effectiveness of the proposed SphereGAN is demonstrated through quantitative and qualitative experiments for unsupervised image generation and 3D point cloud generation, demonstrating its superiority over state-of-the-art GANs with respect to accuracy and convergence on the CIFAR-10, STL-10, LSUN bedroom, and ShapeNet datasets. [ABSTRACT FROM AUTHOR]

We present a novel dehazing framework for real-world images that contain both hazy and low-light areas. Dehazing and low-light enhancements are unified by using an illumination map that is estimated using a proposed convolutional neural network. The illumination map is then used as a component for three different tasks: atmospheric light estimation, transmission map estimation, and low-light enhancement, thereby enabling the solving of interrelated low-level vision problems simultaneously. To train the neural network to perform both dehazing and low-light enhancement, we synthesize hazy and low-light images from normal images. Experimental results demonstrate that the proposed method quantitatively and qualitatively outperforms state-of-the-art algorithms in real-world image dehazing. [ABSTRACT FROM AUTHOR]

The authors present a novel tracking algorithm based on a factorial hidden Markov model (FHMM) that can utilise the structured information of a target. An FHMM consists of multiple hidden Markov models (HMMs), wherein each HMM aims to represent a different part of the target. Then, the geometric relation between patches is encoded in the FHMM framework via either interactive sampling or importance sampling over sets. Experimental results demonstrate that the proposed method qualitatively and quantitatively outperforms other methods, especially when the targets are highly deformable. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel visual tracking method for unmanned aerial vehicles (UAVs) in aerial scenery. To track the UAVs robustly, we present a new object proposal method that can accurately determine the object regions that are likely to exist. The proposed object proposal method is robust to small objects and severe background clutter. For this, we vote on candidate areas of the object and increase or decrease the weight of the area accordingly. Thus, the method can accurately propose the object areas that can be used to track small-sized UAVs with the assumption that their motion is smooth over time. Experimental results verify that UAVs are accurately tracked even when they are very small and the background is complex. The proposed method qualitatively and quantitatively delivers state-of-the-art performance in comparison with conventional object proposal-based methods. [ABSTRACT FROM AUTHOR]

We propose a novel generative adversarial network for class-conditional data augmentation (i.e., GANDA) to mitigate data imbalance problems in image classification tasks. The proposed GANDA generates minority class data by exploiting majority class information to enhance the classification accuracy of minority classes. For stable GAN training, we introduce a new denoising autoencoder initialization with explicit class conditioning in the latent space, which enables the generation of definite samples. The generated samples are visually realistic and have a high resolution. Experimental results demonstrate that the proposed GANDA can considerably improve classification accuracy, especially when datasets are highly imbalanced on standard benchmark datasets (i.e., MNIST and CelebA). Our generated samples can be easily used to train conventional classifiers to enhance their classification accuracy. [ABSTRACT FROM AUTHOR]

In this study, we present a novel tracking system, in which the tracking accuracy can be considerably enhanced by state prediction. Accordingly, we present a new Q-learning-based reinforcement method, augmented by Wang–Landau sampling. In the proposed method, reinforcement learning is used to predict a target configuration for the subsequent frame, while Wang–Landau sampler balances the exploitation and exploration degrees of the prediction. Our method can adapt to control the randomness of policy, using statistics on the number of visits in a particular state. Thus, our method considerably enhances conventional Q-learning algorithm performance, which also enhances visual tracking performance. Numerical results demonstrate that our method substantially outperforms other state-of-the-art visual trackers and runs in realtime because our method contains no complicated deep neural network architectures. [ABSTRACT FROM AUTHOR]

A novel tracking algorithm is proposed, which robustly tracks a target by finding the state that minimizes the likelihood uncertainty. Likelihood uncertainty is estimated by determining the gap between the lower and upper bounds of likelihood. By minimizing the gap between the two bounds, the proposed method identifies the confident and reliable state of the target. In this study, the state that provides the Minimum Uncertainty Gap (MUG) between likelihood bounds is shown to be more reliable than the state that provides the maximum likelihood only, especially when severe illumination changes, occlusions, and pose variations occur. A rigorous derivation of the lower and upper bounds of the likelihood for the visual tracking problem is provided to address this issue. Additionally, an efficient inference algorithm that uses Interacting Markov Chain Monte Carlo (IMCMC) approach is presented to find the best state that maximizes the average of the lower and upper bounds of likelihood while minimizing the gap between the two bounds. We extend our method to update the target model adaptively. To update the model, the current observation is combined with a previous target model with the adaptive weight, which is calculated according to the goodness of the current observation. The goodness of the observation is measured using the proposed uncertainty gap estimation of likelihood. Experimental results demonstrate that the proposed method robustly tracks the target in realistic videos and outperforms conventional tracking methods. [ABSTRACT FROM AUTHOR]

A novel approach for event summarization and rare event detection is proposed. Unlike conventional methods that deal with event summarization and rare event detection independently, our method solves them in a single framework by transforming them into a graph editing problem. In our approach, a video is represented by a graph, each node of which indicates an event obtained by segmenting the video spatially and temporally. The edges between nodes describe the relationship between events. Based on the degree of relations, edges have different weights. After learning the graph structure, our method finds subgraphs that represent event summarization and rare events in the video by editing the graph, that is, merging its subgraphs or pruning its edges. The graph is edited to minimize a predefined energy model with the Markov Chain Monte Carlo (MCMC) method. The energy model consists of several parameters that represent the causality, frequency, and significance of events. We design a specific energy model that uses these parameters to satisfy each objective of event summarization and rare event detection. The proposed method is extended to obtain event summarization and rare event detection results across multiple videos captured from multiple views. For this purpose, the proposed method independently learns and edits each graph of individual videos for event summarization or rare event detection. Then, the method matches the extracted multiple graphs to each other, and constructs a single composite graph that represents event summarization or rare events from multiple views. Experimental results show that the proposed approach accurately summarizes multiple videos in a fully unsupervised manner. Moreover, the experiments demonstrate that the approach is advantageous in detecting rare transition of events. [ABSTRACT FROM AUTHOR]

We propose the visual tracker sampler, a novel tracking algorithm that can work robustly in challenging scenarios, where several kinds of appearance and motion changes of an object can occur simultaneously. The proposed tracking algorithm accurately tracks a target by searching for appropriate trackers in each frame. Since the real-world tracking environment varies severely over time, the trackers should be adapted or newly constructed depending on the current situation, so that each specific tracker takes charge of a certain change in the object. To do this, our method obtains several samples of not only the states of the target but also the trackers themselves during the sampling process. The trackers are efficiently sampled using the Markov Chain Monte Carlo (MCMC) method from the predefined tracker space by proposing new appearance models, motion models, state representation types, and observation types, which are the important ingredients of visual trackers. All trackers are then integrated into one compound tracker through an Interacting MCMC (IMCMC) method, in which the trackers interactively communicate with one another while running in parallel. By exchanging information with others, each tracker further improves its performance, thus increasing overall tracking performance. Experimental results show that our method tracks the object accurately and reliably in realistic videos, where appearance and motion drastically change over time, and outperforms even state-of-the-art tracking methods. [ABSTRACT FROM AUTHOR]

A novel tracking algorithm is proposed for targets with drastically changing geometric appearances over time. To track such objects, we develop a local patch-based appearance model and provide an efficient online updating scheme that adaptively changes the topology between patches. In the online update process, the robustness of each patch is determined by analyzing the likelihood landscape of the patch. Based on this robustness measure, the proposed method selects the best feature for each patch and modifies the patch by moving, deleting, or newly adding it over time. Moreover, a rough object segmentation result is integrated into the proposed appearance model to further enhance it. The proposed framework easily obtains segmentation results because the local patches in the model serve as good seeds for the semi-supervised segmentation task. To solve the complexity problem attributable to the large number of patches, the Basin Hopping (BH) sampling method is introduced into the tracking framework. The BH sampling method significantly reduces computational complexity with the help of a deterministic local optimizer. Thus, the proposed appearance model could utilize a sufficient number of patches. The experimental results show that the present approach could track objects with drastically changing geometric appearance accurately and robustly. [ABSTRACT FROM AUTHOR]

We propose a novel tracking algorithm based on the Wang-Landau Monte Carlo (WLMC) sampling method for dealing with abrupt motions efficiently. Abrupt motions cause conventional tracking methods to fail because they violate the motion smoothness constraint. To address this problem, we introduce the Wang-Landau sampling method and integrate it into a Markov Chain Monte Carlo (MCMC)-based tracking framework. By employing the novel density-of-states term estimated by the Wang-Landau sampling method into the acceptance ratio of MCMC, our WLMC-based tracking method alleviates the motion smoothness constraint and robustly tracks the abrupt motions. Meanwhile, the marginal likelihood term of the acceptance ratio preserves the accuracy in tracking smooth motions. The method is then extended to obtain good performance in terms of scalability, even on a high-dimensional state space. Hence, it covers drastic changes in not only position but also scale of a target. To achieve this, we modify our method by combining it with the N-fold way algorithm and present the N-Fold Wang-Landau (NFWL)-based tracking method. The N-fold way algorithm helps estimate the density-of-states with a smaller number of samples. Experimental results demonstrate that our approach efficiently samples the states of the target, even in a whole state space, without loss of time, and tracks the target accurately and robustly when position and scale are changing severely. [ABSTRACT FROM AUTHOR]