4 citations · 4 across the 10 of their papers we have counts for
12 papers
Where-OPD: Spatially Guided On-Policy Self-Distillation of MLLMs with Synthetic Scenes
Sophia Sirko-Galouchenko, Monika Wysoczanska, Andrei Bursuc +2
On-policy self-distillation has recently emerged as an effective approach for improving language-model reasoning by supervising students with a frozen or EMA version of themselves…
Spot-the-shift: Evaluating Grounded Image Difference Captioning of Long-term Changes
Benedetta Liberatori, Nermin Samet, Paolo Rota +4
Long-term change understanding from images of the same place revisited over time is a challenging task with applications in map maintenance and urban infrastructure monitoring. Pri…
V-GIFT: Boosting Visual Instruction Tuning with Self-Supervised Guidance
Sophia Sirko-Galouchenko, Monika Wysoczanska, Andrei Bursuc +2
Multimodal large language models (MLLMs) perform well on many vision-language tasks but often struggle with vision-centric problems that require fine-grained visual perception. Rec…
Attention, May I Have Your Decision? Localizing Generative Choices in Diffusion Models
Katarzyna Zaleska, Łukasz Popek, Monika Wysoczańska +1
Text-to-image diffusion models exhibit remarkable generative capabilities, yet their internal operations remain opaque, particularly when handling prompts that are not fully descri…
Test-time Contrastive Concepts for Open-world Semantic Segmentation with Vision-Language Models
Monika Wysoczańska, Antonin Vobecky, Amaia Cardiel +4
Recent CLIP-like Vision-Language Models (VLMs), pre-trained on large amounts of image-text pairs to align both modalities with a simple contrastive objective, have paved the way to…
CLIP-DINOiser: Teaching CLIP a few DINO tricks for open-vocabulary semantic segmentation
Monika Wysoczańska, Oriane Siméoni, Michaël Ramamonjisoa +3
The popular CLIP model displays impressive zero-shot capabilities thanks to its seamless interaction with arbitrary text prompts. However, its lack of spatial awareness makes it un…
Tell Me What Is Good About This Property: Leveraging Reviews For Segment-Personalized Image Collection Summarization
Monika Wysoczanska, Moran Beladev, Karen Lastmann Assaraf +4
Image collection summarization techniques aim to present a compact representation of an image gallery through a carefully selected subset of images that captures its semantic conte…
CLIP-DIY: CLIP Dense Inference Yields Open-Vocabulary Semantic Segmentation For-Free
Monika Wysoczańska, Michaël Ramamonjisoa, Tomasz Trzciński +1
The emergence of CLIP has opened the way for open-world image perception. The zero-shot classification capabilities of the model are impressive but are harder to use for dense task…
Towards Unsupervised Visual Reasoning: Do Off-The-Shelf Features Know How to Reason?
Monika Wysoczańska, Tom Monnier, Tomasz Trzciński +1
Recent advances in visual representation learning allowed to build an abundance of powerful off-the-shelf features that are ready-to-use for numerous downstream tasks. This work ai…
EgoNN: Egocentric Neural Network for Point Cloud Based 6DoF Relocalization at the City Scale
Jacek Komorowski, Monika Wysoczanska, Tomasz Trzcinski
The paper presents a deep neural network-based method for global and local descriptors extraction from a point cloud acquired by a rotating 3D LiDAR. The descriptors can be used fo…
Large-Scale Topological Radar Localization Using Learned Descriptors
Jacek Komorowski, Monika Wysoczanska, Tomasz Trzcinski
In this work, we propose a method for large-scale topological localization based on radar scan images using learned descriptors. We present a simple yet efficient deep network arch…
MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition
Jacek Komorowski, Monika Wysoczanska, Tomasz Trzcinski
We introduce a discriminative multimodal descriptor based on a pair of sensor readings: a point cloud from a LiDAR and an image from an RGB camera. Our descriptor, named MinkLoc++,…