Zobrazeno 1 - 10
of 86
pro vyhledávání: '"Chum, Ondřej"'
This work proposes a novel method for object co-segmentation, i.e. pixel-level localization of a common object in a set of images, that uses no pixel-level supervision for training. Two pre-trained Vision Transformer (ViT) models are exploited: Image
Externí odkaz:
http://arxiv.org/abs/2410.13582
Single-source domain generalization attempts to learn a model on a source domain and deploy it to unseen target domains. Limiting access only to source domain data imposes two key challenges - how to train a model that can generalize and how to verif
Externí odkaz:
http://arxiv.org/abs/2409.19774
Universal image representations are critical in enabling real-world fine-grained and instance-level recognition applications, where objects and entities from any domain must be identified at large scale. Despite recent advances, existing methods fail
Externí odkaz:
http://arxiv.org/abs/2406.08332
Autor:
Psomas, Bill, Kakogeorgiou, Ioannis, Efthymiadis, Nikos, Tolias, Giorgos, Chum, Ondrej, Avrithis, Yannis, Karantzalos, Konstantinos
This work introduces composed image retrieval to remote sensing. It allows to query a large image archive by image examples alternated by a textual description, enriching the descriptive power over unimodal queries, either visual or textual. Various
Externí odkaz:
http://arxiv.org/abs/2405.15587
Publikováno v:
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 11153-11163
Image retrieval methods based on CNN descriptors rely on metric learning from a large number of diverse examples of positive and negative image pairs. Domains, such as night-time images, with limited availability and variability of training data suff
Externí odkaz:
http://arxiv.org/abs/2309.16351
Autor:
Ypsilantis, Nikolaos-Antonios, Chen, Kaifeng, Cao, Bingyi, Lipovský, Mário, Dogan-Schönberger, Pelin, Makosa, Grzegorz, Bluntschli, Boris, Seyedhosseini, Mojtaba, Chum, Ondřej, Araujo, André
Fine-grained and instance-level recognition methods are commonly trained and evaluated on specific domains, in a model per domain scenario. Such an approach, however, is impractical in real large-scale applications. In this work, we address the probl
Externí odkaz:
http://arxiv.org/abs/2309.01858
This work addresses scaling up the sketch classification task into a large number of categories. Collecting sketches for training is a slow and tedious process that has so far precluded any attempts to large-scale sketch recognition. We overcome the
Externí odkaz:
http://arxiv.org/abs/2202.13164
We introduce Object-Guided Localization (OGuL) based on a novel method of local-feature matching. Direct matching of local features is sensitive to significant changes in illumination. In contrast, object detection often survives severe changes in li
Externí odkaz:
http://arxiv.org/abs/2202.04445
Autor:
Papakipos, Zoë, Tolias, Giorgos, Jenicek, Tomas, Pizzi, Ed, Yokoo, Shuhei, Wang, Wenhao, Sun, Yifan, Zhang, Weipu, Yang, Yi, Addicam, Sanjay, Papadakis, Sergio Manuel, Ferrer, Cristian Canton, Chum, Ondrej, Douze, Matthijs
The 2021 Image Similarity Challenge introduced a dataset to serve as a new benchmark to evaluate recent image copy detection methods. There were 200 participants to the competition. This paper presents a quantitative and qualitative analysis of the t
Externí odkaz:
http://arxiv.org/abs/2202.04007
Autor:
Douze, Matthijs, Tolias, Giorgos, Pizzi, Ed, Papakipos, Zoë, Chanussot, Lowik, Radenovic, Filip, Jenicek, Tomas, Maximov, Maxim, Leal-Taixé, Laura, Elezi, Ismail, Chum, Ondřej, Ferrer, Cristian Canton
This paper introduces a new benchmark for large-scale image similarity detection. This benchmark is used for the Image Similarity Challenge at NeurIPS'21 (ISC2021). The goal is to determine whether a query image is a modified copy of any image in a r
Externí odkaz:
http://arxiv.org/abs/2106.09672