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

44 papers

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.…

cs.CV2025

Autoregressive Styled Text Image Generation, but Make it Reliable

Carmine Zaccagnino, Fabio Quattrini, Vittorio Pippi +3

Generating faithful and readable styled text images (especially for Styled Handwritten Text generation - HTG) is an open problem with several possible applications across graphic d…

cs.CV2025

Training-free Online Video Step Grounding

Luca Zanella, Massimiliano Mancini, Yiming Wang +2

Given a task and a set of steps composing it, Video Step Grounding (VSG) aims to detect which steps are performed in a video. Standard approaches for this task require a labeled tr…

cs.CV2025

RefAM: Attention Magnets for Zero-Shot Referral Segmentation

Anna Kukleva, Enis Simsar, Alessio Tonioni +4

Most existing approaches to referring segmentation achieve strong performance only through fine-tuning or by composing multiple pre-trained models, often at the cost of additional…

cs.CV2025

Test-Time Visual In-Context Tuning

Jiahao Xie, Alessio Tonioni, Nathalie Rauschmayr +2

Visual in-context learning (VICL), as a new paradigm in computer vision, allows the model to rapidly adapt to various tasks with only a handful of prompts and examples. While effec…

cs.CV2025★ 1 cited

Zero-Shot Styled Text Image Generation, but Make It Autoregressive

Vittorio Pippi, Fabio Quattrini, Silvia Cascianelli +2

Styled Handwritten Text Generation (HTG) has recently received attention from the computer vision and document analysis communities, which have developed several solutions, either…

cs.CV2025★ 1 cited

Omnia de EgoTempo: Benchmarking Temporal Understanding of Multi-Modal LLMs in Egocentric Videos

Chiara Plizzari, Alessio Tonioni, Yongqin Xian +2

Understanding fine-grained temporal dynamics is crucial in egocentric videos, where continuous streams capture frequent, close-up interactions with objects. In this work, we bring…

cs.CV2024

UIP2P: Unsupervised Instruction-based Image Editing via Edit Reversibility Constraint

Enis Simsar, Alessio Tonioni, Yongqin Xian +2

We propose an unsupervised instruction-based image editing approach that removes the need for ground-truth edited images during training. Existing methods rely on supervised learni…

cs.CV2024

Active Data Curation Effectively Distills Large-Scale Multimodal Models

Vishaal Udandarao, Nikhil Parthasarathy, Muhammad Ferjad Naeem +6

Knowledge distillation (KD) is the de facto standard for compressing large-scale models into smaller ones. Prior works have explored ever more complex KD strategies involving diffe…

cs.CV2024★ 2 cited

BRAVE: Broadening the visual encoding of vision-language models

Oğuzhan Fatih Kar, Alessio Tonioni, Petra Poklukar +3

Vision-language models (VLMs) are typically composed of a vision encoder, e.g. CLIP, and a language model (LM) that interprets the encoded features to solve downstream tasks. Despi…

cs.CV2024★ 1 cited

Snap-it, Tap-it, Splat-it: Tactile-Informed 3D Gaussian Splatting for Reconstructing Challenging Surfaces

Mauro Comi, Alessio Tonioni, Max Yang +5

Touch and vision go hand in hand, mutually enhancing our ability to understand the world. From a research perspective, the problem of mixing touch and vision is underexplored and p…

cs.CV2024★ 4 cited

InseRF: Text-Driven Generative Object Insertion in Neural 3D Scenes

Mohamad Shahbazi, Liesbeth Claessens, Michael Niemeyer +4

We introduce InseRF, a novel method for generative object insertion in the NeRF reconstructions of 3D scenes. Based on a user-provided textual description and a 2D bounding box in…

cs.CV2023

Text-Conditioned Resampler For Long Form Video Understanding

Bruno Korbar, Yongqin Xian, Alessio Tonioni +2

In this paper we present a text-conditioned video resampler (TCR) module that uses a pre-trained and frozen visual encoder and large language model (LLM) to process long video sequ…

cs.CV2023

LIME: Localized Image Editing via Attention Regularization in Diffusion Models

Enis Simsar, Alessio Tonioni, Yongqin Xian +2

Diffusion models (DMs) have gained prominence due to their ability to generate high-quality varied images with recent advancements in text-to-image generation. The research focus i…

cs.CV2023

TouchSDF: A DeepSDF Approach for 3D Shape Reconstruction using Vision-Based Tactile Sensing

Mauro Comi, Yijiong Lin, Alex Church +3

Humans rely on their visual and tactile senses to develop a comprehensive 3D understanding of their physical environment. Recently, there has been a growing interest in exploring a…

cs.CV2023★ 3 cited

TextMesh: Generation of Realistic 3D Meshes From Text Prompts

Christina Tsalicoglou, Fabian Manhardt, Alessio Tonioni +2

The ability to generate highly realistic 2D images from mere text prompts has recently made huge progress in terms of speed and quality, thanks to the advent of image diffusion mod…