64 citations · 211 across the 53 of their papers we have counts for
7 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…
From MLP to NeoMLP: Leveraging Self-Attention for Neural Fields
Miltiadis Kofinas, Samuele Papa, Efstratios Gavves
Neural fields (NeFs) have recently emerged as a state-of-the-art method for encoding spatio-temporal signals of various modalities. Despite the success of NeFs in reconstructing in…
Dream to Manipulate: Compositional World Models Empowering Robot Imitation Learning with Imagination
Leonardo Barcellona, Andrii Zadaianchuk, Davide Allegro +3
A world model provides an agent with a representation of its environment, enabling it to predict the causal consequences of its actions. Current world models typically cannot direc…
Any-Resolution AI-Generated Image Detection by Spectral Learning
Dimitrios Karageorgiou, Symeon Papadopoulos, Ioannis Kompatsiaris +1
Recent works have established that AI models introduce spectral artifacts into generated images and propose approaches for learning to capture them using labeled data. However, the…
CaPo: Cooperative Plan Optimization for Efficient Embodied Multi-Agent Cooperation
Jie Liu, Pan Zhou, Yingjun Du +4
In this work, we address the cooperation problem among large language model (LLM) based embodied agents, where agents must cooperate to achieve a common goal. Previous methods ofte…
Language Agents Meet Causality -- Bridging LLMs and Causal World Models
John Gkountouras, Matthias Lindemann, Phillip Lippe +2
Large Language Models (LLMs) have recently shown great promise in planning and reasoning applications. These tasks demand robust systems, which arguably require a causal understand…