117 citations · 124 across the 9 of their papers we have counts for
9 papers
HSA: Hierarchical Slot Attention for Multi-granularity Scene-Decomposition
Neelu Madan, Rongzhen Zhao, Andreas Mogelmose +4
Slot attention is a powerful framework for object-centric learning, decomposing visual scenes into latent slots through iterative competitive attention. However, existing methods s…
Metrics or Mirage? An Audit of Evaluation Inconsistencies in Colonoscopy Polyp Segmentation Benchmarks
Aisha Urooj, Zain Ul Abdien, Neelu Madan
Progress in colonoscopy polyp segmentation is routinely reported through leaderboard comparisons on a small set of public benchmarks. We argue that this apparent progress is diffic…
Out of Context: Reliability in Multimodal Anomaly Detection Requires Contextual Inference
Kevin Wilkinghoff, Neelu Madan, Juan Miguel Valverde +6
Anomaly detection aims to identify observations that deviate from expected behavior. Because anomalous events are inherently sparse, most frameworks are trained exclusively on norm…
A Hyperbolic Perspective on Hierarchical Structure in Object-Centric Scene Representations
Neelu Madan, Àlex Pujol, Andreas Møgelmose +4
Slot attention has emerged as a powerful framework for unsupervised object-centric learning, decomposing visual scenes into a small set of compact vector representations called \em…
SlotMatch: Distilling Object-Centric Representations for Unsupervised Video Segmentation
Diana-Nicoleta Grigore, Neelu Madan, Andreas Mogelmose +2
Unsupervised video segmentation is a challenging computer vision task, especially due to the lack of supervisory signals coupled with the complexity of visual scenes. To overcome t…
Foundation Models for Video Understanding: A Survey
Neelu Madan, Andreas Moegelmose, Rajat Modi +2
Video Foundation Models (ViFMs) aim to learn a general-purpose representation for various video understanding tasks. Leveraging large-scale datasets and powerful models, ViFMs achi…
CL-MAE: Curriculum-Learned Masked Autoencoders
Neelu Madan, Nicolae-Catalin Ristea, Kamal Nasrollahi +2
Masked image modeling has been demonstrated as a powerful pretext task for generating robust representations that can be effectively generalized across multiple downstream tasks. T…
Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection
Neelu Madan, Nicolae-Catalin Ristea, Radu Tudor Ionescu +4
Anomaly detection has recently gained increasing attention in the field of computer vision, likely due to its broad set of applications ranging from product fault detection on indu…
Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection
Nicolae-Catalin Ristea, Neelu Madan, Radu Tudor Ionescu +4
Anomaly detection is commonly pursued as a one-class classification problem, where models can only learn from normal training samples, while being evaluated on both normal and abno…