output
20082026
most citedMachine Learning based System for Vessel Turnaround Time Prediction

30 citations

30 papers

cs.CV2026

ENEAS: Embedding-guided Neural Ensemble for Adaptive Segmentation

Javier del Pino, Salvador Rodríguez, Alejandro Garabito +2

We present ENEAS, a unified, text-promptable method for instance tracking and semantic discovery. Text-promptable segmentation models, including the latest foundation models such a…

eess.SP2026

Contours-Seeking Proposal Density Particle Filter and Resilient Terrain-Referenced Navigation

Junwoo Park, Hyochoong Bang

Auxiliary navigation systems are essential for the robust operation of aerial vehicles, particularly in self-contained frameworks like terrain-referenced navigation. However, chall…

cs.HC2026

Quantifying the Cost of Manual Navigation: A Comparison of Gesture-Based Magnification versus Direct Access Reading in Digital Layout-based Documents

Sebastián Gallardo, Hui-Yin Wu, Dorian Mazauric +4

Understanding how diverse audiences engage with structured media is critical to ensure a consistent quality of experience. In this context, we quantify the behavioral and performan…

cs.LG2026

Designing a double deep reinforcement learning selection tool for resilient demand prediction

Bilel Abderrahmane Benziane, Benoit Lardeux, Ayoub Mcharek +1

The use of artificial intelligence in supply chain forecasting has attracted many scientific studies for several decades. However, the process of selecting an appropriate forecasti…

cs.CL2026

A Browser-based Open Source Assistant for Multimodal Content Verification

Rosanna Milner, Michael Foster, Twin Karmakharm +5

Disinformation and false content produced by generative AI pose a significant challenge for journalists and fact-checkers who must rapidly verify digital media information. While t…

cs.IR2026

LILaC: Late Interacting in Layered Component Graph for Open-domain Multimodal Multihop Retrieval

Joohyung Yun, Doyup Lee, Wook-Shin Han

Multimodal document retrieval aims to retrieve query-relevant components from documents composed of textual, tabular, and visual elements. An effective multimodal retriever needs t…