김정욱 교수

Multispectral pedestrian detection has received great attention in recent years as multispectral modalities (i.e. color and thermal) can provide complementary visual information. However, there are major inherent issues in multispectral pedestrian detection. First, the cameras of the two modalities have different field-of-views (FoVs), so that image pairs are often miscalibrated. Second, modality discrepancy is observed, because image pairs are captured at different wavelengths. In this paper, t

Object detection became one of the major fields in computer vision. In object detection, object classification and object localization tasks are conducted. Previous deep learning-based object detection networks perform with feature maps generated by completely shared networks. However, object classification focuses on the most discriminative object part of the feature map. Whereas, object localization requires a feature map that is focused on the entire area of the object. In this paper, we prop

Although the visual appearances of small-scale objects are not well observed, humans can recognize them by associating the visual cues of small objects from their memorized appearance. It is called cued recall. In this paper, motivated by the memory process of humans, we introduce a novel pedestrian detection framework that imitates cued recall in detecting small-scale pedestrians. We propose a large-scale embedding learning with the large-scale pedestrian recalling memory (LPR Memory). The purp

Object detection has received significant interest in the research field of computer vision and is widely used in human-centric applications. The occlusion problem is a frequent obstacle that degrades detection quality. In this paper, we propose a novel object detection framework targeting robust object detection in occlusion. The proposed deep learning-based network consists mainly of two parts: 1) object detection framework, which classifies the object categories and localizes the object locat

Recently, automated surveillance cameras can change a visible sensor and a thermal sensor for all-day operation. However, existing single-modal pedestrian detectors mainly focus on detecting pedestrians in only one specific modality (i.e., visible or thermal), so they cannot cope with other modal inputs. In addition, recent multispectral pedestrian detectors have shown remarkable performance by adopting multispectral modalities, but they also have limitations in practical applications (e.g., dif

Object detection in a road scene has received a significant attention from research fields of developing autonomous vehicle and automatic road monitoring systems. However, object occlusion problems frequently occur in generic road scenes. Due to such occlusion problems, previous object detection methods have limitations of not being able to detect objects accurately. In this paper, we propose a novel object detection network which is robust in occlusions. For effective object detection even with

Recently, a wide range of research on object detection has shown breakthrough performance. However, in a challenging environment, such as occlusion and small object cases, object detectors still produce inaccurate or erroneous predictions. To effectively cope with such conditions, most of the existing methods have suggested loss functions to guide the object detectors by modulating the magnitude of their loss. However, when modulating the loss function, they are highly dependent on the classific

Monocular 3D object detection has drawn increasing attention in various human-related applications, such as autonomous vehicles, due to its cost-effective property. On the other hand, a monocular image alone inherently contains insufficient information to infer the 3D information. In this paper, we propose a new monocular 3D object detector that can recall the stereoscopic visual information about an object, given a left-view monocular image. Here, we devise a location embedding module to handle

Object detection performs two tasks (classification and localization) simultaneously. Two tasks share a similarity: they need robust features that effectively represent the visual appearance of the objects. However, two tasks also have different properties. First, classification mainly requires features from discriminative parts of an object to determine the object category, whereas localization mainly requires features from the entire object regions for localizing by drawing a bounding box. Sec

The performance of recent deep neural networks in various computer vision areas such as object detection has increased significantly. Along with such advances, attempts to visualize and interpret the networks have been made in order to understand how a network predicts a certain result. However, there is a lack of research on ways to improve the interpretability of networks’ features. In this paper, we propose a spatial relation reasoning (SRR) framework to encode interpretable networks’ feature

현재 국내의 문화 교육은 ‘다문화’라는 거대 담론 하에 교육 대상이 내국인이아닌 여성결혼이민자와 그 자녀들 내지 이주근로자로 구성되어 있다. 그 과정에서 한국인은 문화교육에 있어 타자에 위치에 자리하게 되었다. 특히 전통문화의경우는 교육대상과 상관없이 교육수요가 적어 전통의 가치를 그대로 담으면서흥미요소를 갖춘 콘텐츠의 계발이 필요한 실정인데 본고는 새로운 콘텐츠를 제작하자는 것이 아니라 이미 만들어진 작품을 가지고 교육 자료로 활용을 할 수있어야 한다는 측면에서 논의를 전개했다. 네이버 웹툰 <신과 함께>는 우리 신화와 전통적인 저승관을 현대적 감각으로 그려낸 작품으로 작품성과 흥행성 모두에서 성공했다는 평가를 받고 있다. 삶과 죽음은 연속된 현상으로 어떤 인생을 살았는지가 어떻게 죽음을 받아들일지에 영향을 미친다. 즉 ‘인생관’과 ‘사후관’은 서로 영향을 미치는데 일생의례 중상례는 아직까지 전통적 모습을 대부분 갖추고 있기 때문에 과거와 현재의 소통에 중요한 매개가 될 수 있다.

In this paper, we propose a novel medical image segmentation using iterative deep learning framework. We have combined an iterative learning approach and an encoder-decoder network to improve segmentation results, which enables to precisely localize the regions of interest (ROIs) including complex shapes or detailed textures of medical images in an iterative manner. The proposed iterative deep convolutional encoder-decoder network consists of two main paths: convolutional encoder path and convol

Although audio modality has the potential to solve various visually challenging conditions of visual modality, there are few studies on audio-based detection. This is because the audio modality itself contains less accurate spatial information. To alleviate this issue, the existing audio-based methods adopt the visual modality in the training phase to transfer more precise spatial knowledge to the audio modality. However, they do not consider the case where the visual modality is less informativ