publications

2026

1. CVPR

   Franca: Nested matryoshka clustering for scalable visual representation learning

   Shashanka Venkataramanan^*, Valentinos Pariza^*, Mohammadreza Salehi, Lukas Knobel, Spyros Gidaris, Elias Ramzi, Andrei Bursuc, and Yuki M Asano

   In CVPR 2026, 2026

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   We present Franca (pronounced Fran-ka): free one; the first fully open-source (data, code, weights) vision foundation model that matches and in many cases surpasses the performance of state-of-the-art proprietary models, e.g., DINOv2, CLIP, SigLIPv2, etc. Our approach is grounded in a transparent training pipeline inspired by Web-SSL and uses publicly available data: ImageNet-21K and a subset of ReLAION-2B. Beyond model release, we tackle critical limitations in SSL clustering methods. While modern models rely on assigning image features to large codebooks via clustering algorithms like Sinkhorn-Knopp, they fail to account for the inherent ambiguity in clustering semantics. To address this, we introduce a parameter-efficient, multi-head clustering projector based on nested Matryoshka representations. This design progressively refines features into increasingly fine-grained clusters without increasing the model size, enabling both performance and memory efficiency. Additionally, we propose a novel positional disentanglement strategy that explicitly removes positional biases from dense representations, thereby improving the encoding of semantic content. This leads to consistent gains on several downstream benchmarks, demonstrating the utility of cleaner feature spaces. Our contributions establish a new standard for transparent, high-performance vision models and open a path toward more reproducible and generalizable foundation models for the broader AI community.

2024

1. CVPR

   Learning to Count without Annotations

   Lukas Knobel, Tengda Han^*, and Yuki M. Asano^*

   In CVPR 2024, 2024

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   While recent supervised methods for reference-based object counting continue to improve the performance on benchmark datasets, they have to rely on small datasets due to the cost associated with manually annotating dozens of objects in images. We propose UnCounTR, a model that can learn this task without requiring any manual annotations. To this end, we construct "Self-Collages", images with various pasted objects as training samples, that provide a rich learning signal covering arbitrary object types and counts. Our method builds on existing unsupervised representations and segmentation techniques to successfully demonstrate for the first time the ability of reference-based counting without manual supervision. Our experiments show that our method not only outperforms simple baselines and generic models such as FasterRCNN and DETR, but also matches the performance of supervised counting models in some domains.

2023

1. ICCV

   Geometric Superpixel Representations for Efficient Image Classification with Graph Neural Networks

   Radu A Cosma^*, Lukas Knobel^*, Putri A Linden, David M Knigge, and Erik J Bekkers

   In ICCV 2023 - Visual Inductive Priors for Data-Efficient Deep Learning Workshop, 2023

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   While Convolutional Neural Networks and Vision Transformers are the go-to solutions for image classification, their model sizes make them expensive to train and deploy. Alternatively, input complexity can be reduced following the intuition that adjacent similar pixels contain redundant information. This prior can be exploited by clustering such pixels into superpixels and connecting adjacent superpixels with edges, resulting in a sparse graph representation on which Graph Neural Networks (GNNs) can operate efficiently. Although previous work clearly highlights the computational efficiency of this approach, this prior can be overly restrictive and, as a result, performance is lacking compared to contemporary dense vision methods. In this work, we propose to extend this prior by incorporating shape information into the individual superpixel representations. This is achieved through a separate, patch-level GNN. Together with enriching the previously explored appearance and pose information of superpixels and further architectural changes, our best model, ShapeGNN, surpasses the previous state-of-the-art in superpixel-based image classification on CIFAR-10 by a significant margin. We also present an optimised pipeline for efficient image-to-graph transformation and show the viability of training end-to-end on high-resolution images on ImageNet-1k.

2022

1. ReScience

   Reproducibility study of "Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling"

   Sarah De Boer^*, Radu Alexandru Cosma^*, Lukas Knobel^*, Yeskendir Koishekenov^*, and Benjamin Shaffrey^*

   In ML Reproducibility Challenge 2021 (Fall Edition), 2022

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   Our work attempts to verify two methods to mitigate forms of inequality in ride‐pooling platforms proposed in the paper Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling: (1) integrating fairness constraints into the objective functions and (2) redistributing income of drivers. We extend this paper by testing for robustness to a change in the neighbourhood selection process by using actual Manhattan neighbour‐hoods and we use corresponding demographic data to examine differences in service based on ethnicity.