5 citations · 8 across the 12 of their papers we have counts for
8 papers · 1 filter
Redefining Normal: A Novel Object-Level Approach for Multi-Object Novelty Detection
Mohammadreza Salehi, Nikolaos Apostolikas, Efstratios Gavves +2
In the realm of novelty detection, accurately identifying outliers in data without specific class information poses a significant challenge. While current methods excel in single-o…
Lost in Time: A New Temporal Benchmark for VideoLLMs
Daniel Cores, Michael Dorkenwald, Manuel Mucientes +2
Large language models have demonstrated impressive performance when integrated with vision models even enabling video understanding. However, evaluating video models presents its o…
Better Language Models Exhibit Higher Visual Alignment
Jona Ruthardt, Gertjan J. Burghouts, Serge Belongie +1
How well do text-only large language models (LLMs) align with the visual world? We present a systematic evaluation of this question by incorporating frozen representations of vario…
SelEx: Self-Expertise in Fine-Grained Generalized Category Discovery
Sarah Rastegar, Mohammadreza Salehi, Yuki M. Asano +2
In this paper, we address Generalized Category Discovery, aiming to simultaneously uncover novel categories and accurately classify known ones. Traditional methods, which lean heav…
Near, far: Patch-ordering enhances vision foundation models' scene understanding
Valentinos Pariza, Mohammadreza Salehi, Gertjan Burghouts +2
We introduce NeCo: Patch Neighbor Consistency, a novel self-supervised training loss that enforces patch-level nearest neighbor consistency across a student and teacher model. Comp…
SIGMA: Sinkhorn-Guided Masked Video Modeling
Mohammadreza Salehi, Michael Dorkenwald, Fida Mohammad Thoker +3
Video-based pretraining offers immense potential for learning strong visual representations on an unprecedented scale. Recently, masked video modeling methods have shown promising…
GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Features
Luc P. J. Sträter, Mohammadreza Salehi, Efstratios Gavves +2
In the domain of anomaly detection, methods often excel in either high-level semantic or low-level industrial benchmarks, rarely achieving cross-domain proficiency. Semantic anomal…
PIN: Positional Insert Unlocks Object Localisation Abilities in VLMs
Michael Dorkenwald, Nimrod Barazani, Cees G. M. Snoek +1
Vision-Language Models (VLMs), such as Flamingo and GPT-4V, have shown immense potential by integrating large language models with vision systems. Nevertheless, these models face c…