267 citations · 284 across the 7 of their papers we have counts for
7 papers
VINO: Video-driven Invariance for Non-contextual Objects via Structural Prior Guided De-contextualization
Seul-Ki Yeom, Marcel Simon, Eunbin Lee +1
Self-supervised learning (SSL) has made rapid progress, yet learned features often over-rely on contextual shortcuts-background textures and co-occurrence statistics. While video p…
Video Self-Distillation for Single-Image Encoders: A Step Toward Physically Plausible Perception
Marcel Simon, Tae-Ho Kim, Seul-Ki Yeom
Self-supervised image encoders such as DINO have recently gained significant interest for learning robust visual features without labels. However, most SSL methods train on static…
UniForm: A Reuse Attention Mechanism Optimized for Efficient Vision Transformers on Edge Devices
Seul-Ki Yeom, Tae-Ho Kim
Transformer-based architectures have demonstrated remarkable success across various domains, but their deployment on edge devices remains challenging due to high memory and computa…
U-MixFormer: UNet-like Transformer with Mix-Attention for Efficient Semantic Segmentation
Seul-Ki Yeom, Julian von Klitzing
Semantic segmentation has witnessed remarkable advancements with the adaptation of the Transformer architecture. Parallel to the strides made by the Transformer, CNN-based U-Net ha…
Automatic Neural Network Pruning that Efficiently Preserves the Model Accuracy
Thibault Castells, Seul-Ki Yeom
Neural networks performance has been significantly improved in the last few years, at the cost of an increasing number of floating point operations per second (FLOPs). However, mor…
Toward Compact Deep Neural Networks via Energy-Aware Pruning
Seul-Ki Yeom, Kyung-Hwan Shim, Jee-Hyun Hwang
Despite the remarkable performance, modern deep neural networks are inevitably accompanied by a significant amount of computational cost for learning and deployment, which may be i…
Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning
Seul-Ki Yeom, Philipp Seegerer, Sebastian Lapuschkin +4
The success of convolutional neural networks (CNNs) in various applications is accompanied by a significant increase in computation and parameter storage costs. Recent efforts to r…