이상윤

Digital screen photography frequently suffers from moiré patterns caused by frequency aliasing between camera sensors and display pixels. Removing moiré artifacts is challenging due to their varying color, shape, and scale. Existing methods primarily operate in the spatial domain and thus overlook crucial frequency-domain information inherent in moiré patterns. To address this, we propose Spectrally Empowered Demoiréing Transformers (SEDT), a U-shaped Transformer integrating a Frequency Domain Modulator (FDM) within each Transformer block. The FDM performs spectral filtering through learnable modulation of frequency components, such as channel-wise affine transformations and band-wise frequency masking, enabling effective extraction of moiré-related features across multiple scales and frequency ranges. Evaluations on the TIP-2018 dataset demonstrate that SEDT significantly outperforms existing state-of-the-art models in both PSNR and SSIM, highlighting the potential of frequency-domain enhancements in Transformer architectures for advanced image restoration tasks. The code is available at https://github.com/sylee-skhu/SEDT.

Robust fall detection is critical in safety-sensitive contexts such as elderly care and industrial environments. Recent fall detection methods leverage 2D human pose estimation, which encodes posture as compact skeletal keypoints. However, these systems are vulnerable to performance degradation when body parts are occluded or keypoints are missing, often due to environmental constraints or inaccuracies in pose estimation. To address this, we propose a fall detection framework based on single-frame 2D human pose estimation and a skeleton-aware graph attention network (StoneGAT). StoneGAT enhances conventional graph attention network (GAT) by incorporating edge features based on bone-related information, such as bone lengths, joint angles, and confidence-based metrics. In addition, we introduce a training strategy named PointOut, which probabilistically drops node features during training to encourage structure-aware learning, thereby improving model robustness. Experiments on a combined dataset of AI-hub and in-house dataset demonstrate that StoneGAT with PointOut outperforms baseline models, including standard MLP, graph convolutional network (GCN) and GAT, especially under severe occlusion. Ablation studies confirm the effectiveness of edge attributes and the PointOut strategy.

In this study, we present the Discriminative Temporal Shift Module (D-TSM), an enhancement of the Temporal Shift Module (TSM) for action recognition. TSM has limitations in capturing intricate temporal dynamics due to its simplistic feature shifting. D-TSM addresses this by introducing a subtraction operation before the shifting. This enables the extraction of discriminative features between adjacent frames, thereby allowing for effective action recognition where subtle motions serve as crucial cues. It preserves TSM???foundational philosophy, prioritizing minimal computational overhead and no additional parameters. Our experiments demonstrate that D-TSM significantly improves performance of TSM and outperforms other leading 2D CNN-based methods.

When multiple object tracking (MOT) based on the tracking-by-detection paradigm is implemented, the similarity metric between the current detections and existing tracks plays an essential role. Most of the MOT schemes based on a deep neural network learn the similarity metric using a Siamese architecture, but the plain Siamese architecture might not be enough owing to its structural simplicity and lack of motion information. This paper aims to propose a new MOT scheme to overcome the existing problems in the conventional MOTs. Feature pyramid Siamese network (FPSN) is proposed to address the structural simplicity. The FPSN is inspired by a feature pyramid network (FPN) and it extends the Siamese network by applying FPN to the plain Siamese architecture and by developing a new multi-level discriminative feature. A spatiotemporal motion feature is added to the FPSN to overcome the lack of motion information and to enhance the performance in MOT. Thus, FPSN-MOT considers not only the appearance feature but also motion information. Finally, FPSN-MOT is applied to the public MOT challenge benchmark problems and its performance is compared to that of the other state-of-the-art MOT methods.