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L. V. Gool

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Preprint Sep 2026

AHMAD: Adaptive Hybrid Multi-task Vision Learning with Assisted Distillation for Keypoint Detection

Generalist multitasking vision models aim to unify multiple vision tasks within a single framework, enabling more efficient and versatile learning. However, handling diverse vision tasks -- spanning dense and sparse predictions -- remains challenging due to their inherently varying output structures. In this paper, we...

Mohammad Mahdi, Nedyalko Prisadnikov, Yu-Qian Fu et al. · 0 citations
Preprint Sep 2026

Ego-Forge: Text and Geometric-Attention Free Exo-to-Egocentric Video Generation

Ego-Forge is a caption-free and bias-free framework for exo-to-egocentric video generation that achieves state-of-the-art performance on Ego-Exo4D, runs faster end-to-end, requires no external annotation at inference, and generalizes to in-the-wild scenes, including cases where over-reliance on geometry blocks appearan...

Mohammad Mahdi, L. Gool, D. Paudel · 0 citations
Preprint Sep 2026

GraphWrit3R: End-to-End 3D Scene Graph Writing

This work presents GraphWrit3R, a simple end-to-end method that takes a 3D point cloud, Gaussian Splats, or a combination of both as input, and directly outputs a complete scene graph as a structured JSON script, achieving state-of-the-art performance on object class, predicate, and triplet recall on the 3DSSG benchmar...

Luka Milivojevic, Nikola Popovic, Sayan Deb Sarkar et al. · 0 citations
Preprint Aug 2026

Event-Based Motion Estimation via Oriented Distance Fields

Oriented Distance Field Motion Estimation (ODF Motion Estimation), which replaces this optimization with a single averaging step over a precomputed field of event distance vectors, combined with an adaptive event-count selection strategy and a parameter-free trail filter, reaches sub-pixel accuracy at the lowest latenc...

Lei Sun, Yuqin Ma, Weilun Li et al. · 0 citations
Jul 2026

Knowledge-Centric Agents for Workflow Generation in ComfyUI

This work argues that successful workflow generation requires modeling knowledge itself, including its structure, hierarchy, and reasoning dynamics, and proposes a knowledge-centric framework that learns to invert, inject, and infer with knowledge across multiple abstraction levels.

Zhen-Dong Li, Lei Sun, Ruibo Ming et al. · 2 citations

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