5 citations · 8 across the 12 of their papers we have counts for
12 papers · 1 filter
MC-PanDA++: Simpler, Stronger, and More Robust Domain-Adaptive Panoptic Segmentation
Ivan Martinović, Josip Šarić, Yuki M. Asano +1
Unsupervised domain adaptation (UDA) reduces the annotation burden in panoptic segmentation by leveraging a cost-effectively labeled source domain (e.g., synthetic) and an unlabele…
Fourier Self-Supervision for Fine-Grained Generalized Category Discovery
Sarah Rastegar, Mina Ghadimi Atigh, Pascal Mettes +2
Generalized Category Discovery aims to recognize known categories while identifying novel ones within unlabeled data. Existing methods, typically based on self-supervision and cont…
MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning
Mohammadreza Salehi, Shashanka Venkataramanan, Ioana Simion +3
Dense self-supervised learning has shown great promise for learning pixel- and patch-level representations, but extending it to videos remains challenging due to the complexity of…
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…
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…
Motion Flow Matching for Human Motion Synthesis and Editing
Vincent Tao Hu, Wenzhe Yin, Pingchuan Ma +7
Human motion synthesis is a fundamental task in computer animation. Recent methods based on diffusion models or GPT structure demonstrate commendable performance but exhibit drawba…
Guided Diffusion from Self-Supervised Diffusion Features
Vincent Tao Hu, Yunlu Chen, Mathilde Caron +3
Guidance serves as a key concept in diffusion models, yet its effectiveness is often limited by the need for extra data annotation or classifier pretraining. That is why guidance w…