21 citations · 60 across the 39 of their papers we have counts for
41 papers
From Visual Cues to Spoken Narration: Rethinking Audio Description
Akshita Gupta, Aditya Arora, Federico Tombari +2
Audio Description (AD) provides spoken narration of visual events during dialogue gaps, making movies accessible to visually impaired audiences. The problem requires determining bo…
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…
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…
MOBIUS: Big-to-Mobile Universal Instance Segmentation via Multi-modal Bottleneck Fusion and Calibrated Decoder Pruning
Mattia Segu, Marta Tintore Gazulla, Yongqin Xian +2
Scaling up model size and training data has advanced foundation models for instance-level perception, achieving state-of-the-art in-domain and zero-shot performance across object d…
AnyUp: Universal Feature Upsampling
Thomas Wimmer, Prune Truong, Marie-Julie Rakotosaona +4
We introduce AnyUp, a method for feature upsampling that can be applied to any vision feature at any resolution, without encoder-specific training. Existing learning-based upsample…
Lost in Translation? Vocabulary Alignment for Source-Free Adaptation in Open-Vocabulary Semantic Segmentation
Silvio Mazzucco, Carl Persson, Mattia Segu +4
We introduce VocAlign, a novel source-free domain adaptation framework specifically designed for VLMs in open-vocabulary semantic segmentation. Our method adopts a student-teacher…
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…
Enhancing Self-Supervised Visual Representation Learning via Low-Rank Adapted LLMs
Selim Kuzucu, Muhammad Ferjad Naeem, Anna Kukleva +2
The integration of Large Language Model (LLM) blocks with Vision Transformers (ViTs) holds significant promise for vision-only tasks by leveraging the rich semantic knowledge and r…
One2Any: One-Reference 6D Pose Estimation for Any Object
Mengya Liu, Siyuan Li, Ajad Chhatkuli +3
6D object pose estimation remains challenging for many applications due to dependencies on complete 3D models, multi-view images, or training limited to specific object categories.…
LaRI: Layered Ray Intersections for Single-view 3D Geometric Reasoning
Rui Li, Biao Zhang, Zhenyu Li +2
We present Layered Ray Intersections (LaRI), a fully supervised method for occluded geometry reasoning from a single image. Unlike conventional depth estimation, which is limited t…
Semantic Library Adaptation: LoRA Retrieval and Fusion for Open-Vocabulary Semantic Segmentation
Reza Qorbani, Gianluca Villani, Theodoros Panagiotakopoulos +8
Open-vocabulary semantic segmentation models associate vision and text to label pixels from an undefined set of classes using textual queries, providing versatile performance on no…
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…
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…
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…
LoRACLR: Contrastive Adaptation for Customization of Diffusion Models
Enis Simsar, Thomas Hofmann, Federico Tombari +1
Recent advances in text-to-image customization have enabled high-fidelity, context-rich generation of personalized images, allowing specific concepts to appear in a variety of scen…
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…
TokenFormer: Rethinking Transformer Scaling with Tokenized Model Parameters
Haiyang Wang, Yue Fan, Muhammad Ferjad Naeem +5
Transformers have become the predominant architecture in foundation models due to their excellent performance across various domains. However, the substantial cost of scaling these…
Self-supervised Shape Completion via Involution and Implicit Correspondences
Mengya Liu, Ajad Chhatkuli, Janis Postels +2
3D shape completion is traditionally solved using supervised training or by distribution learning on complete shape examples. Recently self-supervised learning approaches that do n…
Toward a Diffusion-Based Generalist for Dense Vision Tasks
Yue Fan, Yongqin Xian, Xiaohua Zhai +4
Building generalized models that can solve many computer vision tasks simultaneously is an intriguing direction. Recent works have shown image itself can be used as a natural inter…
Stylebreeder: Exploring and Democratizing Artistic Styles through Text-to-Image Models
Matthew Zheng, Enis Simsar, Hidir Yesiltepe +3
Text-to-image models are becoming increasingly popular, revolutionizing the landscape of digital art creation by enabling highly detailed and creative visual content generation. Th…
How Good is my Video LMM? Complex Video Reasoning and Robustness Evaluation Suite for Video-LMMs
Muhammad Uzair Khattak, Muhammad Ferjad Naeem, Jameel Hassan +4
Recent advancements in Large Language Models (LLMs) have led to the development of Video Large Multi-modal Models (Video-LMMs) that can handle a wide range of video understanding t…
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…
Know Your Neighbors: Improving Single-View Reconstruction via Spatial Vision-Language Reasoning
Rui Li, Tobias Fischer, Mattia Segu +3
Recovering the 3D scene geometry from a single view is a fundamental yet ill-posed problem in computer vision. While classical depth estimation methods infer only a 2.5D scene repr…
Contrastive Test-Time Composition of Multiple LoRA Models for Image Generation
Tuna Han Salih Meral, Enis Simsar, Federico Tombari +1
Low-Rank Adaptation (LoRA) has emerged as a powerful and popular technique for personalization, enabling efficient adaptation of pre-trained image generation models for specific ta…