1 citations · 1 across the 9 of their papers we have counts for
9 papers
Autoresearch with Coding Agents: Generalizers and Metric-Maximizers on Quran Recitation Data
Nursultan Askarbekuly, Mohamad Al Mdfaa, Ahmed Helaly +2
Coding agents can now be left alone to improve software against a score. In this pattern--recently popularized as "autoresearch"--the agent receives a dataset, an evaluation script…
MuViSeg: Multi-View Segment Correspondences from Dense Geometry Priors
Denis Fatykhoph, Timur Akhtyamov, Konstantin Pakulev +2
Object-level mapping and topological navigation reason about whole objects, yet the correspondences they rely on are computed over keypoints or pixels and only aggregated into obje…
VL-MemKnG: Hybrid Memory with a Spatio-Temporal Knowledge Graph for Question Answering over Long Egocentric Navigation Trajectories
Svetlana Lukina, Mohamad Al Mdfaa, Gloria Haro +2
Answering navigation-relevant questions over long egocentric videos requires retrieving and organizing evidence distributed across distant temporal moments while maintaining spatia…
DynoSLAM: Dynamic SLAM with Generative Graph Neural Networks for Real-World Social Navigation
Danil Tokhchukov, Veronika Morozova, Gonzalo Ferrer
Traditional Simultaneous Localization and Mapping (SLAM) algorithms rely heavily on the static environment assumption, which severely limits their applicability in real-world space…
CADFS: A Big CAD Program Dataset and Framework for Computer-Aided Design with Large Language Models
Vladislav Pyatov, Gleb Bobrovskikh, Saveliy Galochkin +6
We introduce CADFS, a data-centric framework that enables large vision-language models to generate complex CAD design histories. Existing generative CAD systems are restricted to s…
Spatiotemporal Knowledge Graphs as Persistent Scene Memory for Embodied Question Answering
Mohamad Al Mdfaa, Svetlana Lukina, Timur Akhtyamov +4
Vision-language models (VLMs) demonstrate strong image-level scene understanding, but reasoning over long egocentric video remains costly: because VLMs maintain no persistent memor…
PixelNav: Towards Model-based Vision-Only Navigation with Topological Graphs
Sergey Bakulin, Timur Akhtyamov, Denis Fatykhov +2
This work proposes a novel hybrid approach for vision-only navigation of mobile robots, which combines advances of both deep learning approaches and classical model-based planning…
EgoWalk: A Multimodal Dataset for Robot Navigation in the Wild
Timur Akhtyamov, Mohamad Al Mdfaa, Javier Antonio Ramirez Benavides +11
Data-driven navigation algorithms are critically dependent on large-scale, high-quality real-world data collection for successful training and robust performance in realistic and u…
Mapping the Unseen: Unified Promptable Panoptic Mapping with Dynamic Labeling using Foundation Models
Mohamad Al Mdfaa, Raghad Salameh, Geesara Kulathunga +2
Panoptic maps enable robots to reason about both geometry and semantics. However, open-vocabulary models repeatedly produce closely related labels that split panoptic entities and…