162 citations · 421 across the 9 of their papers we have counts for
10 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…
Her2 Challenge Contest: A Detailed Assessment of Automated Her2 Scoring Algorithms in Whole Slide Images of Breast Cancer Tissues
Talha Qaiser, Abhik Mukherjee, Chaitanya Reddy Pb +19
Evaluating expression of the Human epidermal growth factor receptor 2 (Her2) by visual examination of immunohistochemistry (IHC) on invasive breast cancer (BCa) is a key part of th…
Generalized orderless pooling performs implicit salient matching
Marcel Simon, Yang Gao, Trevor Darrell +2
Most recent CNN architectures use average pooling as a final feature encoding step. In the field of fine-grained recognition, however, recent global representations like bilinear p…
ImageNet pre-trained models with batch normalization
Marcel Simon, Erik Rodner, Joachim Denzler
Convolutional neural networks (CNN) pre-trained on ImageNet are the backbone of most state-of-the-art approaches. In this paper, we present a new set of pre-trained models with pop…
Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of Convolutional Neural Networks Approaches
Erik Rodner, Marcel Simon, Robert B. Fisher +1
In this paper, we study the sensitivity of CNN outputs with respect to image transformations and noise in the area of fine-grained recognition. In particular, we answer the followi…