Ismini Lourentzou
7 indexed papers
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The paper introduces VisAnomReasoner, a parameter-efficient Vision-Language Model (VLM), trained on a new benchmark (VisAnomBench) to accurately and interpretably detect anomalies in time-series data.
The paper introduces VisAnomReasoner, a parameter-efficient Vision-Language Model (VLM), trained on a new benchmark (VisAnomBench) to accurately and interpretably detect anomalies in time-series data.
This paper proposes RECONTEXT, a training-free inference method for improving long-context reasoning in large language models using model-internal relevance signals and recursive evidence replay.
The paper introduces ELSA3D, a unified 3D model that uses elastic semantic anchoring to improve interaction between text and 3D representations, achieving state-of-the-art performance with reduced FLOPs and inference latency.
This paper introduces GraphVid, a graph-conditioned image-to-video generation model enabling precise multi-subject control through structured interaction graphs, and curates GraphVid-Bench, a large-scale interaction-centric video dataset.
This paper introduces SILSA, a topology-aware 3D generation framework that improves structural fidelity and reduces generation cost.
The paper presents CoDA, a framework for co-evolving simulation data and an action policy to improve robot environment construction and policy learning.
Papers
CoDA: Co-Evolution of Data Engines and Action Policies
Tianjiao Yu, Zhilin Ou, Yifan Shen, Xinzhuo Li +4 more
The paper presents CoDA, a framework for co-evolving simulation data and an action policy to improve robot environment construction and policy learning.