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Giorgos Tolias

27 accepted papers

2026

Attention, Please! Revisiting Attentive Probing Through the Lens of Efficiency

Bill Psomas, Dionysis Christopoulos, Eirini Baltzi, Ioannis Kakogeorgiou, Tilemachos Aravanis, Nikos Komodakis +3
ICLR 2026poster

As fine-tuning becomes impractical at scale, probing is emerging as the preferred evaluation protocol. However, standard linear probing can understate the capability of models whose pre-training optimizes local representations rather than an explicit global representation. This motivates attentive p…

Cited by 0Source ↗code ↗Scholar ↗
2026

ELViS: Efficient Visual Similarity from Local Descriptors that Generalizes Across Domains

Pavel Suma, Giorgos Kordopatis-Zilos, Yannis Kalantidis, Giorgos Tolias
ICLR 2026poster

Large-scale instance-level training data is scarce, so models are typically trained on domain-specific datasets. Yet in real-world retrieval, they must handle diverse domains, making generalization to unseen data critical. We introduce ELViS, an image-to-image similarity model that generalizes effec…

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2026

Retrieve and Segment: Are a Few Examples Enough to Bridge the Supervision Gap in Open-Vocabulary Segmentation?

Tilemachos Aravanis, Vladan Stojnić, Bill Psomas, Nikos Komodakis, Giorgos Tolias
CVPR 2026

Open-vocabulary segmentation (OVS) extends the zero-shot recognition capabilities of vision-language models (VLMs) to pixel-level prediction, enabling segmentation of arbitrary categories specified by text prompts. Despite recent progress, OVS lags behind fully supervised approaches due to two chall

Cited by 0Source ↗code ↗Scholar ↗
2026

SPAR: Single-Pass Any-Resolution ViT for Open-vocabulary Segmentation

Naomi Kombol, Ivan Martinović, Siniša Šegvić, Giorgos Tolias
CVPR 2026

Foundational Vision Transformers (ViTs) have limited effectiveness in tasks requiring fine-grained spatial understanding, due to their fixed pre-training resolution and inherently coarse patch-level representations. These challenges are especially pronounced in dense prediction scenarios, such as op

Cited by 0Source ↗code ↗Scholar ↗
2025

A Dataset for Semantic Segmentation in the Presence of Unknowns

Zakaria Laskar, Tomas Vojir, Matej Grcic, Iaroslav Melekhov, Shankar Gangisetty, Juho Kannala +3
CVPR 2025poster

Before deployment in the real-world deep neural networks require thorough evaluation of how they handle both knowns, inputs represented in the training data, and unknowns (anomalies). This is especially important for scene understanding tasks with safety critical applications, such as in autonomous…

Cited by 1Source ↗PDF ↗code ↗Scholar ↗
2025

ILIAS: Instance-Level Image retrieval At Scale

Giorgos Kordopatis-Zilos, Vladan Stojnić, Anna Manko, Pavel Suma, Nikolaos-Antonios Ypsilantis, Nikos Efthymiadis +4
CVPR 2025poster

This work introduces ILIAS, a new test dataset for Instance-Level Image retrieval At Scale. It is designed to evaluate the ability of current and future foundation models and retrieval techniques to recognize particular objects. The key benefits over existing datasets include large scale, domain div…

Cited by 1Source ↗PDF ↗Scholar ↗
2025

Instance-Level Composed Image Retrieval

Bill Psomas, George Retsinas, Nikos Efthymiadis, Panagiotis Filntisis, Yannis Avrithis, Petros Maragos +2
NeurIPS 2025poster

The progress of composed image retrieval (CIR), a popular research direction in image retrieval, where a combined visual and textual query is used, is held back by the absence of high-quality training and evaluation data. We introduce a new evaluation dataset, i-CIR, which, unlike existing datasets,…

Cited by 0Source ↗Scholar ↗
2025

LOCORE: Image Re-ranking with Long-Context Sequence Modeling

Zilin Xiao, Pavel Suma, Ayush Sachdeva, Hao-Jen Wang, Giorgos Kordopatis-Zilos, Giorgos Tolias +1
CVPR 2025poster

We introduce LOCORE, Long-Context Re-ranker, a model that takes as input local descriptors corresponding to an image query and a list of gallery images and outputs similarity scores between the query and each gallery image. This model is used for image retrieval, where typically a first ranking is p…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2025

LPOSS: Label Propagation Over Patches and Pixels for Open-vocabulary Semantic Segmentation

Vladan Stojnić, Yannis Kalantidis, Jiří Matas, Giorgos Tolias
CVPR 2025poster

We propose a training-free method for open-vocabulary semantic segmentation using Vision-and-Language Models (VLMs). Our approach enhances the initial per-patch predictions of VLMs through label propagation, which jointly optimizes predictions by incorporating patch-to-patch relationships. Since VLM…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2025

Processing and acquisition traces in visual encoders: What does CLIP know about your camera?

Ryan Ramos, Vladan Stojnić, Giorgos Kordopatis-Zilos, Yuta Nakashima, Giorgos Tolias, Noa Garcia
ICCV 2025poster

Prior work has analyzed the robustness of visual encoders to image transformations and corruptions, particularly in cases where such alterations are not seen during training. When this occurs, they introduce a form of distribution shift at test time, often leading to performance degradation. The pri…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2024

AMES: Asymmetric and Memory-Efficient Similarity Estimation for Instance-level Retrieval

Pavel Suma*, Giorgos Kordopatis-Zilos, Ahmet Iscen, Giorgos Tolias
ECCV 2024poster

"This work investigates the problem of instance-level image retrieval re-ranking with the constraint of memory efficiency, ultimately aiming to limit memory usage to 1KB per image. Departing from the prevalent focus on performance enhancements, this work prioritizes the crucial trade-off between per…

Cited by 5Source ↗PDF ↗code ↗Scholar ↗
2024

Label Propagation for Zero-shot Classification with Vision-Language Models

Vladan Stojni?, Yannis Kalantidis, Giorgos Tolias
CVPR 2024poster

Vision-Language Models (VLMs) have demonstrated impressive performance on zero-shot classification i.e. classification when provided merely with a list of class names. In this paper we tackle the case of zero-shot classification in the presence of unlabeled data. We leverage the graph structure of t…

Cited by 10Source ↗PDF ↗code ↗Scholar ↗
2023

Test-time Training for Matching-based Video Object Segmentation

Juliette Bertrand, Giorgos Kordopatis-Zilos, Yannis Kalantidis, Giorgos Tolias
NeurIPS 2023poster

The video object segmentation (VOS) task involves the segmentation of an object over time based on a single initial mask. Current state-of-the-art approaches use a memory of previously processed frames and rely on matching to estimate segmentation masks of subsequent frames. Lacking any adaptation m…

Cited by 6Source ↗PDF ↗Scholar ↗
2021

The Met Dataset: Instance-level Recognition for Artworks

Nikolaos-Antonios Ypsilantis, Noa Garcia, Guangxing Han, Sarah Ibrahimi, Nanne Van Noord, Giorgos Tolias
NeurIPS 2021poster

This work introduces a dataset for large-scale instance-level recognition in the domain of artworks. The proposed benchmark exhibits a number of different challenges such as large inter-class similarity, long tail distribution, and many classes. We rely on the open access collection of The Met museu…

Cited by 47Source ↗Scholar ↗
2020

Graph convolutional networks for learning with few clean and many noisy labels

Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, Ondřej Chum, Cordelia Schmid
ECCV 2020poster

In this work we consider the problem of learning a classifier from noisy labels when a few clean labeled examples are given. The structure of clean and noisy data is modeled by a graph per class and Graph Convolutional Networks (GCN) are used to predict class relevance of noisy examples. For each cl…

Cited by 25Source ↗PDF ↗code ↗Scholar ↗
2020

Learning and Aggregating Deep Local Descriptors for Instance-level Recognition

Giorgos Tolias, Tomas Jenicek, Ondřej Chum
ECCV 2020poster

We propose an efficient method to learn deep local descriptors for instance-level recognition. The training only requires examples of positive and negative image pairs and is performed as metric learning of sum-pooled global image descriptors. At inference, the local descriptors are provided by the…

Cited by 121Source ↗PDF ↗code ↗Scholar ↗
2019

Targeted Mismatch Adversarial Attack: Query With a Flower to Retrieve the Tower

Giorgos Tolias, Filip Radenovic, Ondrej Chum
ICCV 2019poster

Access to online visual search engines implies sharing of private user content -- the query images. We introduce the concept of targeted mismatch attack for deep learning based retrieval systems to generate an adversarial image to conceal the query image. The generated image looks nothing like the u…

Cited by 79PDF ↗code ↗Scholar ↗
2018

Fast Spectral Ranking for Similarity Search

Ahmet Iscen, Yannis Avrithis, Giorgos Tolias, Teddy Furon, Ondřej Chum
CVPR 2018poster

Despite the success of deep learning on representing images for particular object retrieval, recent studies show that the learned representations still lie on manifolds in a high dimensional space. This makes the Euclidean nearest neighbor search biased for this task. Exploring the manifolds online…

Cited by 66Source ↗PDF ↗Scholar ↗
2018

Mining on Manifolds: Metric Learning Without Labels

Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, Ondřej Chum
CVPR 2018poster

In this work we present a novel unsupervised framework for hard training example mining. The only input to the method is a collection of images relevant to the target application and a meaningful initial representation, provided e.g. by pre-trained CNN. Positive examples are distant points on a sing…

Cited by 145Source ↗PDF ↗code ↗Scholar ↗
2018

Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking

Filip Radenović, Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, Ondřej Chum
CVPR 2018poster

In this paper we address issues with image retrieval benchmarking on standard and popular Oxford 5k and Paris 6k datasets. In particular, annotation errors, the size of the dataset, and the level of challenge are addressed: new annotation for both datasets is created with an extra attention to the r…

Cited by 542Source ↗PDF ↗Scholar ↗
2017

Efficient Diffusion on Region Manifolds: Recovering Small Objects With Compact CNN Representations

Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, Teddy Furon, Ondrej Chum
CVPR 2017poster

Query expansion is a popular method to improve the quality of image retrieval with both conventional and CNN representations. It has been so far limited to global image similarity. This work focuses on diffusion, a mechanism that captures the image manifold in the feature space. An efficient off-lin…

Cited by 233PDF ↗code ↗Scholar ↗