activity
20172026
most citedReal-Time Highly Accurate Dense Depth on a Power Budget using an FPGA-CPU Hybrid SoC

16 citations · 39 across the 32 of their papers we have counts for

collaborators
Showing 2026Show all

9 papers · 1 filter

cs.CV2026

The First EgoCross Challenge at EgoVis 2026: Cross-Domain Egocentric Video Question Answering

Yuqian Fu, Tianwen Qian, Yanjun Li +30

EgoCross is a cross-domain egocentric video question answering benchmark designed to evaluate whether multimodal large language models can generalize beyond common daily-life scena…

cs.CV2026

Editing Everything Everywhere All at Once

Fabio Quattrini, Carmine Zaccagnino, Enis Simsar +4

Editing multiple elements of an image in a single forward pass is a practical alternative to multi-turn image manipulation, offering improved efficiency and potentially better harm…

cs.CV2026

DataComp-VLM: Improved Open Datasets for Vision-Language Models

Matteo Farina, Vishaal Udandarao, Thao Nguyen +33

Building performant Vision-Language Models (VLMs) requires carefully curating large-scale training datasets, yet the community lacks systematic benchmarks for evaluating such curat…

cs.CV2026

PARCEL: Pool-Anchored Resampling with Conditioned Elastic Queries for Efficient Vision-Language Understanding

Selim Kuzucu, Alessio Tonioni, Vasile Lup +3

Large Vision-Language Models (LVLMs) map visual inputs into dense token sequences, imposing a quadratic computational bottleneck for inference. Elastic visual-token compression add…

cs.CV2026

FullFlow: Upgrading Text-to-Image Flow Matching Models for Bidirectional Vision--Language Generation

Eric Tillmann Bill, Enis Simsar, Alessio Tonioni +1

Modern text-to-image diffusion models encode rich visual priors, but expose them only through one-way text-conditioned generation. Existing unified vision--language models derived…

cs.CV2026

SSL-R1: Self-Supervised Visual Reinforcement Post-Training for Multimodal Large Language Models

Jiahao Xie, Alessio Tonioni, Nathalie Rauschmayr +2

Reinforcement learning (RL) with verifiable rewards (RLVR) has demonstrated the great potential of enhancing the reasoning abilities in multimodal large language models (MLLMs). Ho…

cs.CV2026

R-CoV: Region-Aware Chain-of-Verification for Alleviating Object Hallucinations in LVLMs

Jiahao Xie, Alessio Tonioni, Nathalie Rauschmayr +2

Large vision-language models (LVLMs) have demonstrated impressive performance in various multimodal understanding and reasoning tasks. However, they still struggle with object hall…

cs.CV2026

Shifting the Breaking Point of Flow Matching for Multi-Instance Editing

Carmine Zaccagnino, Fabio Quattrini, Enis Simsar +4

Flow matching models have recently emerged as an efficient alternative to diffusion, especially for text-guided image generation and editing, offering faster inference through cont…

cs.CV2026

MOSAIC-GS: Monocular Scene Reconstruction via Advanced Initialization for Complex Dynamic Environments

Svitlana Morkva, Maximum Wilder-Smith, Michael Oechsle +3

We present MOSAIC-GS, a novel, fully explicit, and computationally efficient approach for high-fidelity dynamic scene reconstruction from monocular videos using Gaussian Splatting.…